essay advertisement analysis

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16 Drafting Your Ad Analysis

Dr. Karen Palmer

Now that you have a solid outline, it’s time to start writing your ad analysis paper! Here we will work through fleshing out each part of your outline–turning your outline into a full draft.

Introduction

The first part of your paper is your introduction. You may remember from the Writing Formula chapter that an introduction consists of three main parts: the hook, the introduction to the topic, and the thesis. Let’s begin with the hook. A hook does two jobs–it connects the topic of your paper to your readers, and it attempts to capture their attention.

This video highlights some of the most common techniques for writing a good hook:

Now that you have a general idea of what a hook does, let’s focus in on the kind of hook that would be most useful for your ad analysis essay. Let’s say you are doing an analysis on that milk ad we discussed earlier in the text.

Strategy 1: Connect to the topic of the ad: milk. You could say something like, “Do you drink milk?” But…would that really draw in readers? Surely, there is a better way to grab the attention of our audience.

Strategy 2: Connect to the broader topic of advertising. Here you might say something like, “Advertisers are always trying to get our attention.” Sure, this is a broad opening to the paper, but is it really going to make anyone interested in the topic?

A good idea is to brainstorm some current events or topics that link to your ad. A brainstorming list for this milk ad could include lactose intolerance, the concept of looking at TV sitcom characters as role models, the changing role of mothers, and even the pressure placed on moms (and women in general)  to be perfect. Choose something that appeals to you and that illustrates a theme that runs through the ad. When brainstorming with my classes, we often land on the idea of perfection with this particular milk ad. It makes a compelling frame for the paper.

Introducing the topic is just that–letting readers know what the paper will be about. ie An ad for ________ located in _________ magazine illustrates this concept. Note that you need to include the specific product advertised in the ad, the name of the magazine in which the ad is located, and include a connection/transition to your hook.

Finally, the last sentence of your introduction is your thesis. Here you make your argument. While you already wrote a thesis for your outline, you want to double check that the thesis connects in some way to your hook. Our example thesis is: “The advertisers successfully persuade the consumer that milk will make them a great mom by using nostalgia, milk branding, and the image of ideal motherhood.” We might make a slight adjustment here to make the connection a bit more explicit: “The advertisers play on the desire of moms to fulfill an image of perfection by using nostalgia, milk branding, and the image of ideal motherhood.”

In the ad analysis, our background consists of two different sections: the description and the discussion of context.

Description

Remember that your audience cannot see the ad you are discussing. If you were in a room presenting to your audience, you might project an image of the ad up on a screen. Since we can’t do that in an essay, we need to describe the ad for our readers. Essentially, you want your readers to be able to draw a basic picture of your ad–or at least visualize it accurately in their minds.

This video from James Rath discussing how people with visual impairments see images on social media gives an important life reason for learning how to write solid image descriptions:

Here are some good tips for writing a description of an image:

1. Start by giving readers a one sentence overview of the ad. For our milk ad, that might be, “In this ad, three mothers from iconic sitcoms sit side by side in a beauty parlor under old-fashioned hair dryers.”

2. Determine in advance how you want readers to see the image–do you want them to look at the image left to right? Foreground to background? Clockwise? Bottom line here–don’t make readers minds jump around from place to place as they try to visualize the image.

3. Choose the key elements. You don’t have to describe every single thing in this paragraph. Tell readers who the three moms are and what show they are from. Give enough basic details so that readers know the setting is old-fashioned. Remember, you’ll be able to bring forward more detail as you analyze the ad in the body of your paper. Readers don’t need to know what color a person’s eyes are unless it’s a key part of the ad.

4. Don’t forget the text! While you should not write every word in the ad in your description, especially if there are lengthy paragraphs, you should include a brief overview of the text. ie placement, basic overview Again, you’ll be able to give specific quotes that are relevant to your analysis in the body of your paper.

5. Write in present tense!

The context of an ad really focuses on the audience of the ad. Remember that advertisers very carefully consider the audience for their product and create their advertisements to best reach that target audience. Let’s look at this from the perspective of a company looking to place an ad:

So, if an advertiser goes to this much trouble to determine the demographics of their target audience, it’s obviously important! The ad (unless perhaps it was published by an inexperienced advertiser) is not “for everyone.” An ad in Newsweek , no matter how childlike it appears, was not created for children. It was created for the audience who will purchase and read this magazine. When we do an ad analysis, we want to share similar information with our readers. What magazine is the ad placed in? What is the general focus of that publication? What kinds of articles appear in the publication? What general types of ads appear? In short, who is the audience? Of course, you can look at a magazine and get some of this information. You can also do a quick online search for the demographics of the magazine or for their media kit, which is what advertisers look at prior to purchasing advertising space to ensure the magazine is a good fit for their ad.

Now that you have the background out of the way and your audiences thoroughly understand the topic, it’s time to begin your analysis. Your thesis should have given at least three advertising strategies used in the ad. Your paper should include a paragraph for each one of those strategies.

Topic Sentence

The topic sentence should echo the wording of the thesis and clearly introduce the topic. For example, “One way the advertisers use the concept of the perfect mother to convince readers to purchase milk is by using iconic mothers from television shows.” For your next paragraph, you’d want to be sure to include a transition. For example, “Another way” or “In addition to” are both phrases that can be used to show that you are building onto your previous paragraph.

In this part of the paragraph, you want to give specific examples from the ad to support your point.

First, you should introduce the example. “The three moms from iconic tv shows are the focus of this ad.”

Next, you should give specific examples from the ad–this could be pointing out particular details about the images in the ad or quoting from the text–or both! For example, for the milk ad, you might give the specific names of the characters and the shows they are from. You might point out that every detail of their outfits are perfect. That they are wearing makeup and jewelry. That they have their wedding rings prominently focused in the image. You might also quote text, like the line from the ad that says, “Another all-time great mom line.”

Finally, wrap up your examples with a clear explanation of how the example proves your point. For example, you might say that, especially in modern times, it is very difficult for mothers to live up to the standard of perfection set by these three television moms. You might explain how causing readers to feel “less than” sets the stage for them to accept the premise that giving their children milk will make them more like these TV moms.

The wrap up for your paragraph is similar to the wrap up for the evidence provided. Here you want to reiterate your thesis in a simple sentence. For example, you might say, “Using the images of these iconic moms convinces moms that, in order to be a good mom, they must buy milk for their children.”

The conclusion of your paper is essentially a mirror image of your introduction. Think of your paper as an Oreo cookie. The introduction and the conclusion are the cookies that surround the best part–the body of the paper. Like the cookie outsides of the Oreo, the introduction and conclusion should be mirror images of each other.

1. Start with re-stating the thesis.

2. Reiterate the topic.

3. Return to your hook and elaborate.

Unlike an Oreo, the conclusion should not simply copy your introduction word for word in a different order. Try to restate your sentences in a different way. Elaborate on your hook so that you leave readers with something to think about!

 Content written by Dr. Karen Palmer and is licensed CC BY NC.

The Worry Free Writer Copyright © 2020 by Dr. Karen Palmer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Advertisement Analysis – How to Write & Ad Analysis Essay Examples

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In this day and age, advertising is everywhere, from billboards and TV commercials to social media feeds and mobile apps. It’s an essential tool many companies use to draw customers’ attention and showcase their products and services. However, creating a compelling and distinctive advertisement is more challenging than it seems, and professionals often rely on ad analysis to achieve this goal. Advertisement analysis is a form of research that examines advertisements’ effectiveness and impact on society. Below, we will discuss how advertisement analysis can help businesses develop successful ad campaigns while ensuring their ads are ethical and socially responsible.

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Ad analysis is a type of research that experts use to develop compelling and eye-catching advertisements . It addresses each step of the ad’s creation process. Such an approach has become increasingly common because it shows marketing techniques’ impact on human consciousness. Experts evaluate the effectiveness of an ad using qualitative and quantitative methods , which help them create better advertisements. Language, imagery, and music used in a successful marketing campaign are just a few examples of what makes up effective ad messaging.

How to analyze the advertisement? While every company and its marketing team may have their own approach to ad analysis, the framework usually includes these 5 major steps:

Gather information. Before starting a project, looking up information about the product is vital. Make a SWOT analysis of the company for which you are conducting an ad analysis. This method will help you identify potential market opportunities and internal weaknesses.

Find target-audience preferences. To choose the perfect media tools for your marketing campaign, you must know your ad’s target audience . Knowing your audience will also assist you in learning how to convince the customers to get interested and purchase the product you are advertising.

Start questioning. You have to create a list of detailed inquiries regarding the advertisement. These questions will aid in finding information about the message or context of the ad . Also, it will help you understand which areas require more research and improvement.

Examine the strategic and tactical components. During this step, you first need to identify the objective. Make sure the message is conveyed clearly so the advertisement can serve its intended purpose. Then, you need to identify the target message. It’ll help to create a brief messaging framework.

Onlook the results. You have to watch whether your advertisement analysis works or not. Analyze how many new customers you receive after publication and your product’s popularity level. That way, you will both improve your research and gain experience for your next project.

Here you can find 2 incredible examples of advertisement analysis essays! The primary focus of each report is to examine how the created advertisement will affect potential customers.

Essay sample #1 – Pepsi advertisement

Target Audience: Pepsi targets consumers in their teens, early 20s, and early middle age. Pepsi print is of bright color , and that instantly attracts customers’ attention. In the commercial, many young people with happy smiles enjoy life, skating on the board and drinking Pepsi.

Implicit messages: The appearance of joyful teens in the Pepsi ad makes you want to buy this drink. The advertisement suggests that after consuming the product, you’ll feel like you’re living your best life.

Essay sample #2 – YSL perfume advertisement

Target Audience: YSL perfume advertisement targets women of early middle age. In the ad, the women are confident, independent, and successful. The advertisement connects the sensation of freedom and high status in society to the perfume itself.

Implicit messages: The advertisement appeals to those who want to make their own rules. YSL customers are women, so the company creates an image of powerful yet feminine females. The commercial suggests that after buying the perfume, you will embrace freedom and will be able to set old bridges on fire.

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Advertisement Analysis Essay: Steps, Tips, Insights, & Example

Conventional selling methods that entail rational thoughts are no longer effective.

Today, advertisements that motivate the viewer or reader to take deliberate action stimulate emotion.

Therefore, knowing how to write an advertisement analysis essay correctly is an essential skill that all marketing or business students should master before graduation.

Advertisement analysis essays, also known as ad analysis essays, are quite popular among students.

Such essays are more about ad reviewing and have a specific format that should be adhered to.

What is an advertisement analysis essay, and how do you correctly write one? Keep on reading to find out more.

What is an Advertisement Analysis Essay?

An advertisement analysis essay is an academic essay that needs the student or writer to study an advert properly.

The essay is typically written about a television or print commercial, and it aims to disclose any hidden messages featured in the advertisement which might be misleading or false.

This can be achieved through studying different aspects like gender, used color schemes, age of the target market, and even the genre of music featured, among other things.

For instance, you can highlight how advertising primarily gives males dominant positions over women through virtually all details displayed in the advert.

A counterpart will then have to examine the same advert from the standpoint that it treats both genders equally, thus eliminating any preconceived thoughts about gender discrimination.

Nonetheless, even though ad analysis essays focus on specific works, whether visual or print, the analysis can be stretched to cover how media is used in audience manipulation.

You can, for instance, have an ad analysis essay that compares and contrasts gender roles across different ads or TV programs like soap operas and commercials.

And one great advantage that this kind of essay has over other essays written on the same topic is its ability to use several sources in backing and supporting an argument, and this not only shows that you have conducted thorough research on the topic but also proves your point.

Steps for Writing a Critical Analysis Essay for an Advertisement

Writing an advertisement essay is as simple as keenly reading or observing the advert and then interpreting its meaning to the target audience or exploring how well a brand or a company uses the Ad to achieve its marketing functions.

Today, there are many ways to run adverts apart from print media. Online platforms such as Facebook, TikTok, Instagram, and YouTube allow influencers, companies/brands, and marketing agencies to run different ads. Besides, there is also a choice of running ads on commercial TV or radio.

Like a standard academic essay , specific steps should be followed when writing an ad analysis essay.

Below are the steps involved in writing an ad analysis essay like a pro!

Step One: Analyze The Chosen Ad

You can look through magazines or newspapers to find one to discuss if not already provided. Pick an advertisement you understand and have sufficient background information on. Knowing the different parts of an ad and a few advertising methods will help you develop a comprehensive analysis and informative essay.

What five parts of an ad should you look out for? They are;

• A captivating headline
• Relatable color schemes, images, as well as packaging that capture the consumer's interest
• Marketing the benefits
• A call to action
• A memorable tagline

When assessing the advertisement, observe specific factors like the language, graphics, target audience, message, and cultural significance. In addition, the utilized advertising techniques should also be examined.

Step Two: Use Your Introduction to Introduce the Ad

The first sentence of your introduction should be an attention-grabber/hook that attracts your readers. It can be a statement, observation, statistic, or fact.

After selecting and analyzing the specific advertisement, utilize your essay's introduction to offer background details on the service or product presented in the ad.

Next, give a short analysis of the ad's history, mention why the advert seems better than others, and discuss the target audience.

Step Three: Add Your Thesis Statement

Utilize your thesis to mention what the essay will highlight and what the selected advert is doing about achieving its goal. The thesis statement should include the ad's message, whether it is implicit or explicit.

Make sure that the thesis statement is the last sentence in your introduction. A good thesis statement lets the reader know your standpoint before reading the entire essay.

Step Four: Discuss One Point Per Paragraph

Each body paragraph featured in the body of your essay should discuss one central point. For example, you can discuss the ad's creativity in one paragraph and then discuss the methods used to capture attention in another paragraph. This should be elaborate right from your topic sentence to the concluding sentence.

Generally, the body paragraphs should examine the ad and utilize statistics, facts, research, and examples to demonstrate how the advert leads to a specific outcome.

You can, for instance, quote any sensitive language used. Moreover, the body of your essay should explain how the advertising strategies used work and why they were chosen for that particular audience.

You can also compare and contrast the models used in the advert compared to adverts used by competitors to bring in the critical aspect that encouraged a good scholarly discussion.

You should also identify the loopholes in the market that need to be addressed or if there are needs of the target audience that the advert failed to meet.

Every suggestion you make on the advert should be objective and generalized so that the readers can themselves make a subjective opinion.

Do not forget to include examples as well. Besides, you should cite any information you borrow from scholarly sources to avoid plagiarism.

Step Five: Conclude Your Essay

In the essay's conclusion paragraph , summarize your essay, mentioning some of the main points you discussed earlier. You will also need to restate your thesis statement. Remember that the conclusion is one of the most critical parts of your essay. You, therefore, should make sure that it is memorable.

Take advantage of conclusion paragraph starters to write a perfect conclusion that resonates with your readers.

• How to write an outstanding compare and contrast essay . 
• How to write an analytical essay.
• Case study writing process (guide) for college/university students.

Structure of an Ad Analysis Essay

Introduction

The introduction should mention what the advertisement is for. You should summarize the ad's context, name the product or company, and give your thesis statement. The introduction can be written in any of these techniques; an interrogative introduction, narrative introduction, inverted triangle introduction, minding the gap introduction, or a paradoxical introduction.

Your thesis statement should also clarify what the ad is about and who the intended target audience is. Note that the thesis statement should be placed at the end of the introduction. A good thesis statement includes the following:

• Explicit messages ; the obvious and clear messages
• Implicit messages ; the hidden messages. They include the promises made by the ad to the consumer.

Your essay's body paragraphs should utilize evidence from the advertisement to prove the thesis statement. Make sure to include the following in your body paragraphs:

• A short description of the advertisement. You should present an impartial description of the ad's features. You can explain the ad's appearance, what or who is featured, and the different colors used. Remember that this segment should only describe what the reader or reviewer would see, not how the advert works.
• Discuss the target audience and the publication where the ad appeared. Explain what particular group of people the advert is targeting. You should include the race, education, age, sex, class, and marital status of the intended audience.
• Logical appeals/logos. Clearly explain how the advertisement applies logos to appeal to its target audience. Include a few paragraphs to communicate the advert's use of logos.
• Emotional appeal/ pathos. Elaborate on how the advertisement applies emotional appeals to charm its target audience. Include a few paragraphs to communicate the advert's use of pathos.
• Ethical appeals/ ethos. Clearly explain how the advertisement applies ethos to appeal to its target audience. Include a few paragraphs to communicate the advert's use of ethos.

You should provide a brief summary of your essay, mentioning some of the points you discussed earlier. You will need to restate your thesis statement and remember that the conclusion is one of the most critical parts of your essay.

The conclusion should also explain the ad's cultural significance. Mention the attitudes, beliefs, and values the advertisement seeks to meet.

Ad Analysis Essay Outline

It is vital to develop an essay outline before you start writing your paper, and the outline will serve as a plan for how you intend to approach it. Below is an advertisement analysis essay outline template you can use for your assignment.

• The name and purpose of the ad. Include the brand and authors.
• Summary of ad's context.
• Relevant background information about the company or organization featured in the ad.
• The thesis statement.
• The ad's impact on the target audience.

Body paragraphs

• Proof of the ad's effectiveness on the intended audience.
• Mention a few examples (only where applicable).
• Discuss the components of the ad.
• Discuss the approach used by the advertisers.
• Discuss the impact of the advertisement on its audience.
• Logos, pathos, and ethos of the advert.
• Visual and textual strategies used in the ad.
• In case it is a comparison, discuss the similarities and differences.
• Restate the thesis statement.
• Mention what makes the ad stand out.
• Discuss the intention of the ad.
• Give a general reflection on the advertisement and wrap things up with your opinion.

Follow our guidelines, and you can rest assured of having a perfect ad analysis essay!

Sample Advertisement Analysis Essay

Garnier Fructis Shampoo Advertisement Analysis Essay Introduction Fructis Shampoo is one of the major products manufactured by Garnier, an American company. During one of its promotions to market the product, Garnier posted an advertisement for the shampoo in an issue of Cosmopolitan magazine. The ad focuses on a woman's beauty and how important her hair is to her general appearance in society. Like all other ads, the aim of this ad is to convince consumers to buy the product. Per se, the Fructis Shampoo by Garnier ad seeks to appeal to the target market via implicit messages, audience targeting, cultural significance, language, and graphics. Advertisement Analysis (The Body) Women between the ages of 18 to 40 comprise the bulk of Cosmopolitan magazine's target audience. Most of the magazine's readers are enthusiastic about beauty, fashion, and love. The magazine also features different articles on romance, weight loss, and famous personalities. Grownup females mainly read the magazine to be enlightened about current events and to discover solutions to their relationship and physical appearance problems. Through addressing beauty issues, particularly those that involve a woman's hair, this advertisement strongly appeals to women in this target group via implicit messaging. Most American women place great significance on the appearance of their hair and are constantly searching for services or products that will allow them to align their hair to the latest trends in fashion (Zahra et al., 2022). This ad attempts to capitalize on women's worries about their hair by promising them a "great" solution that will enhance their beauty and boost their self-esteem. Therefore, the implicit messages of this ad promise a woman beauty, strength, and confidence. The language employed in the ad expresses ideas about confidence and strength and boosts the promotion of beauty principles. The advert reads "sleek and shine" written in bold. Ladies often link these phrases with good things since American society highly values sleek and shiny hair. The ad is, however, vague regarding how much shine someone's hair will get following the use of the shampoo. In addition, the ad doesn't also define the term "sleek." And even though these two adjectives are appealing, they are useless as the ad does not mention the "shine" and "sleek" levels that the customer should anticipate. So, even though the ad's phrasing has logical appeal or logos (Elfhariyanti et al., 2021), it seems to convey unsupported information about the shampoo. Unfortunately, most readers don't take a moment to consider the significance of these two terms. The graphics utilized in the advert use pathos by emotionally appealing to the intended group. The gorgeous long-haired model featured on the page is the ad's main subject. The model seems to display qualities that most ladies wish to possess. She has long, shining hair, an oval, blemish-free face, and a slim, tiny body. She also appears to be giving the reader an enigmatic, seductive gaze. The model is a woman the magazine readers imagine is sought-after by men and venerated by women, given that she resembles several other women in TV commercials, movies, and shows (Johnson, 2012). As a result, this ad tends to leave the reader with specific ideas about how a woman should physically look to be deemed desirable and beautiful as per the American Culture. With regard to cultural significance, the ad tends to emphasize the importance of physical beauty in American culture, just like other TV programs and adverts do. The ad seems to imply that a lady may only be considered beautiful if she bears similar physical features as the woman featured in the advert. This ad implies that women can only feel secure about their bodies if they have a specific external appearance. Whereas some individuals think a woman ought to be strong, this Garnier ad insinuates that a woman's strength lies in her beauty as per societal standards. And just like other beauty ads, this particular one uses women's insecurities about themselves to get them to purchase cheap products. Ultimately, such advertising highlights a woman's outward beauty while completely overlooking her internal traits like compassion and intelligence. Conclusion The discussed Garnier Fructis shampoo advertisement uses particular appeal elements to draw the target audience's interest hopefully. These elements include implicit messaging, audience targeting, cultural significance, and language and graphics. Even though the use of these particular elements creates considerable appeal to potential buyers, some of these elements depict an exaggerated value of external beauty at the expense of internal beauty. The ad also seems to convey unsubstantiated facts about the product being sold. Therefore, even though the advertisement does a great job of appealing to the target audience, it can be improved to consider women's inner beauty and provide more factual information. References Elfhariyanti*, A. A., Ariyanti, L., & Harti, L. M. (2021). A multimodal analysis: Construing beauty standard in shampoo advertisement.� Pioneer Journal of Language and Literature ,� 13 (1), 134-147. Johnson, F. L. (2012).� Imaging in advertising: Verbal and visual codes of commerce . Routledge. Zahra, G. E., Rehan, M., Hayat, R., & Batool, A. (2022). Construction of beauty concept by beauty product advertisements: A critical discourse analysis.� Journal of Archaeology of Egypt/Egyptology, � 19 (3), 789-804.

How to Start an Advertisement Analysis Essay

Begin by introducing your thesis by explaining the product you picked as your essay's sample. Thoroughly analyze the product and ask your reader or reviewer if they are familiar with the development of the advertised work.

Note that you do not have to agree with the advertisement's implicit message. Discuss your claims in the essay, as there are no wrong or correct answers about the ad's implicit message. However, you will have to support your claims with reasonable arguments.

Next, inform your reader why the advertising company opted to adopt that approach of advertisement for the product you just discussed, given that there are several other modes of advertising. You should aim to detail why and how the company uses that advertisement mode.

Proceed to compare the organization's present ad model with the previous one(s) and its influence on the product's market, loss, or growth. An ad analysis will bring to light the loopholes and gaps in the market. It is vital always to generalize your remarks in the essay so that the reader can form their judgments personally, without your personal views affecting their decision.

Keep in mind that there are different target markets based on the product. Therefore, you must utilize the appropriate methods to communicate your message.

How to Conclude an Ad Analysis Essay

The essay conclusion should include the product's summary, the advertising mode, and how it has affected market changes. To properly conclude your ad analysis essay, summarize the most critical points of your essay. And most important is to restate your thesis statement without using the exact words in the introduction.

You should also rephrase the thesis statement as part of your concluding paragraph to complete the information loop and offer your readers closure.

In addition, mention whether or not the ad achieved its goal of informing, entertaining, or persuading its target audience. And without adding any new information, including one last sentence to leave the reader with something to ponder.

Tips to Write the Best Essay on an Advertisement

• Introduce the subject that you will be advertising. The readers of your analysis might be unfamiliar with the product or service you are discussing. Therefore, introducing it early enough in your essay will make it much simpler to understand. Regardless of the popularity or content of the advertisement, it would help if you gave a brief description of the ad so that everyone has a clear idea of what will be discussed in the essay.
• Establish what audience you'll be addressing. It is vital to know who you are writing to as this will allow you to focus your essay's content appropriately and permit you to draw special attention to those aspects your readers will be most interested in.
• Understand the purpose of the advert and your main reason for writing an analysis essay about that specific advert. Correctly understanding the ad's intent goes a long way in producing a well-structured paper.
• Take time to create an essay outline before you start writing your essay. Note that the contents of your essay need to be presented in a specific order, so you should plan this sequence before you begin writing the essay itself.
• Keep things simple when writing your essay. Avoid the use of complicated jargon. This will make reading more enjoyable and also meaningful.

Summing Up!

Writing an advertisement analysis essay does not have to be as troublesome as you suppose. Rather, it is an interactive process that enables you to get into the creators' minds, explore how well they did their craft, and suggest areas for improvement if needed.

When analyzing an advert, you need to identify the advertisement's rhetorical appeals (ethos, logos, and pathos). You must also analyze the target audience to determine its values, preferences, attitudes, intentions, and beliefs.

Think about the effects or potential purpose of the advertisement using diction, tone, language, and presentation.

You should be critical enough to determine the rhetoric behind the symbols and non-verbal cues and relate them to the specific brand and the target audience.

Now that you have the facts and access to tips, steps, and a written sample advert analysis essay, you are on the right track. However, sometimes many things come our way, limiting our chances to complete writing essays.

If you feel like you could help writing your Ad analysis essay, our English essay helpers can help. We have professional essay writers who specialize in writing critical essays. They have perfected their craft through the years and can write your Ad analysis assignment faster and more efficiently.

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Table of Contents

Collaboration, information literacy, writing process, textual analysis – how to analyze ads.

• © 2023 by Jennifer Janechek - IBM Quantum

Advertising executives and marketing experts more than likely hope that we remain oblivious to the underlying messages that ads contain and that we perceive their work purely from entertainment and consumerist perspectives rather than for the purpose of critical assessment.

But to critically examine the techniques and appeals advertisers use to lure us into supporting certain products, services, claims, or even individuals is an opportunity to hone our analytical skills—skills that enable us to be informed readers of texts and knowledgeable consumers of persuasion. To begin, let’s consider specific words and phrases that can be used in ad analysis:

• Nostalgia: Advertisements for Coca-Cola, summer vacation destinations, or even political candidates can stir up sentiments or memories of “the good old days.” In a commercial, for example, the use of black and white film and/or flashbacks—illustrated by clothes, music, and/or historical events—can invite a specific audience to reflect on the past and evoke a sense of nostalgia.
• Merchants of “cool”: According to PBS, merchants of “cool” are “creators and sellers of popular culture who have made teenagers the hottest consumer demographic in America.”[1]  Such merchants may include Abercrombie & Fitch, Hollister, Hot Topic, and Aéropostale. Each relies on the tween and teen markets to keep its empire in business and markets its definition of “cool” as the coolest when it comes to youth culture.
• The myth of the “ideal you”: Today, in many cases, advertisers still sell their products in a way that invites us to be the “best” versions of ourselves. Cultural stereotypes substantiate this idea of the “best” self, which exists only in the shared imagination of the advertiser and audience.

Analyzing Ads: Socioeconomic Status

To what social class do you belong? How do you know? Can others tell by how you talk, dress, and act? By how much money you have? By your level of education? By your occupation? Despite the presumed cultural ideal of social equality in America, key markers such as income and education are often used for social classification.

Advertisers for many goods and services often frame their rhetorical appeals—their strategies of persuasion—in terms of audiences who are presumed to belong to a particular, often loosely defined, social class. Frequently, these appeals rely on stereotypical qualities associated with various socioeconomic classes. For example, an advertisement for an expensive women’s pant suit may appear in a magazine like Vogue (generally regarded as appealing to an upper-middle-class or upper-class audience) and may feature a svelte, glamorous model unlikely to grace the pages of a flyer for Walmart (generally regarded as appealing to a lower-middle-class or working-class audience). Rhetorical appeals can work on many socioeconomic levels. A relatively expensive perfume like Chanel N° 5 may appeal to members of the lower-middle or working class as a symbol of upward mobility. When analyzing an ad, you might pay close attention to how the ad appeals to you based upon assumptions regarding your socioeconomic status: What rhetorical moves (e.g., tone, composition, dialogue) enact those appeals?

Take, for example, Honda’s “Impossible Dream” commercial:

What might you say about the movement in this commercial? The music? The changes in the model? How does these factors reflect certain assumptions about socioeconomic status, and what do they make you think buying a Honda-brand vehicle will do for a consumer?

Blue Collar versus White Collar

If we are analyzing an advertisement in which a model is working in a construction area digging a ditch, we might discuss the concept of blue-collar work.

Take, for example, this Cheetos ad:

Who is the audience of this commercial? What is the advertiser trying to say about Cheetos: i.e., what will the consumer get from eating Cheetos? What might you say about the ad’s incorporation of construction workers—their movement, their attitudes, etc.? How does the voice of the Cheetos tiger affect the commercial’s message?

On the other hand, if we are analyzing an advertisement in which a professional is depicted in what looks to be a high-powered office, we might discuss the concept of white-collar work. Advertising executives may have chosen those models and work settings in order to speak to a specific audience. That is, issues of socioeconomic status—including income, education, technical skill, dress, race, and gender—may be at play in creating images and scenarios that specific audiences will believe to be realistic in representing a version of reality. Keep in mind that socioeconomic status is a somewhat complex and controversial issue in American society today, particularly with regard to definitions of class levels. If you feel that an advertisement is capitalizing upon socioeconomic stereotypes, why do you think the advertiser has done this? Contrariwise, if an advertisement is resisting stereotypes, what do you think the advertiser is trying to accomplish?

A Checklist for Analyzing Socioeconomic Status in Print Advertisements

• Who appears to be the target audience for the advertisement?
• What seems to be the general tone of the advertisement? Serious? Playful? Satiric?
• Do you notice any other appeals to stereotypes regarding education or income levels (e.g., the “corporate elite,” the “nouveau riche,” or the “literary elite,” who may or may not earn high incomes but wield “power” by virtue of educational or literary achievements)?
• How would you characterize the overall appearance of the models in the ad? If applicable, how would you characterize their clothing? To what social class would you connect each model’s attire? Are brand names evident (e.g., Ralph Lauren, Ecco)? Are the models well-groomed or scruffy? Healthy or unhealthy? Thin and fit or heavy and out of shape? Do the models’ qualities suggest they are from a particular social class? If so, how? Is the advertiser relying on stereotypical characterizations, then? Why do you think the advertiser chose to portray them in these ways?
• What would you guess the average income is of the individuals featured in the ad and/or of the audience to which the ad appeals?
• Do you notice any particular political appeals that may be related to class? With what social class would you associate these appeals and why?
• Does the ad appeal to any stereotypes based on gender or race?  On what evidence do you ground your assumption?  (Refer to the checklists in “Analyzing Ads: Gender” and “Analyzing Ads: Race” for more specific questions on analyzing gender and race in advertisements.)
• If possible, what do you infer to be the highest degree of education that the individuals featured in the ad hold? Also in terms of level of education, who do you believe is the intended audience?
• What is the setting for the advertisement? An elegant spa? A pizza parlor?
• If text appears in the ad, what level of language is used, and for what purpose? Slang? Other informal language? Technical jargon? Standard American English? Dialect? With what class do you associate the use of this level of language? What is the effect of language use in this advertisement?
• Are symbols, metaphors, hyperbole, allusions, and/or other forms of figurative language used? If so, what is the effect? Does the use of figurative language evoke appeals to class in any way?
• What appeals to ethos, pathos, and logos do you find? Are these appeals related to class issues? Do you notice the use of any logical fallacies related to class issues (e.g., ad hominem, the slippery slope)? How effective are they?
• In what ways does the advertisement appeal to class? Is the goal of the ad to encourage consumers to spend for the purpose of obtaining, or acquiring the appearance of, a higher socioeconomic status? (Examples of such strategy might be ads for a BMW or a Porsche that suggest the consumer would be more likely to attract members of the opposite sex if he or she were to purchase the advertised car.) Or, does the ad urge individuals to pursue an elite status (e.g., an American Express credit card) that will provide the illusion of upward class mobility.

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Analysis Essay On An Advertisement (Writing Guide)

How to write good analysis essay on an advertisement.

Outline example

How to start, introduction example.

• How to write the thesis statement

Thesis example

How to write body paragraphs, example of body paragraphs.

• How to conclude

Conclusion example

• Revision tips

Advertising plays a major role in our society today; everywhere you go you will find products being advertised on television, online pages, billboards. Advertisement analysis is a common assignment students are required to undertake. Writing an analysis of an advertisement is more about writing a review of the advertisement using a specific format. There are several strategies to go about this type of assignment. So, below is a step-by-step approach to writing an analysis of an advertisement.

Introduction :

• What is the advertisement for
• Summary of the context of the advertisement
• Background information about the company
• The thesis statement
• The effect of the advertisement and the target audience

Body Paragraphs :

• Present evidence of the effectiveness of the ad on the target audience
• Give examples
• Show various components of the advertisement
• Explain some of the outstanding strategies used to persuade the target audience
• Describe the values and emotion the ad provokes in the readers
• Describe the visual strategies
• Describe the ethos, pathos, and logos
• Describe the textual strategies, including the diction and the tone.

Conclusion :

• Present the most important points justify why the advertisement is successful
• The present technique used that makes the product outstanding
• Review the intention of the advertisement
• Provide your opinion.

In the introduction, it is important to state what the analysis will focus on. The ideas to get to the point as early as possible. The essay writer should not assume that the readers are familiar with the product. That is why the first step is to analyze if the advertisement presents a brief history and a detailed description of what the product is about. A good advertisement needs to show how the product is superior to other products in the market.

For example, when a company produces a commercial the aim is to increase sales.

• Here are also points you should consider when writing your essay:
• Some people prefer to write the introduction after they have written the essay itself – you should try both ways to see which one works better for you.
• The introduction must always contain the thesis statement.
• Any information which is needed for the essay, but doesn’t necessarily fit into any of the body paragraphs, should go into the introduction.
• Don’t make any arguments in the introduction itself; save it for the body paragraphs.
• The introduction should summarise the main arguments you intend to make.

Now, you know the main rules of writing an introduction. Next, please find an example of the introduction.

Old Spice’s advertisement “How Your Man Could Smell Like” is an attractive phrase used to lure the audience to purchase the product. The advertisement meant to capture men’s attention through women. It presents an ideal image of how a man should smell. The advertisement used sexually themed strategy to grab the reader’s attention.

How to write a thesis statement

To write a thesis statement, make sure that you have done all the research you want to do, and that you know everything you want to when it comes to your essay. Try and boil down the ultimate point of the essay into a small amount of space – at the most two sentences. It should be clear enough that every part of your essay will be able to relate to it without much trouble.

The advertisement conveys a strong message about a strong personality where a man needs not only to be attractive but also to be confident by smelling like a real man. The advertisement uses emotional appeal to influence young women who value strong qualities in a man.

Any advertisement is meant for a specific audience, therefore, a good analysis should present the target audience. The body paragraphs should clearly present, which groups of people are being targeted, discusses how the intention presented work together to create a good impression. When writing an advertisement analysis essay, it is important to explain how popular and effective the advertisement is. Describe the rhetorical appeals, including pathos, ethos, and logo, these are concepts that provoke emotion among the target audience in an attempt to convince them to like the product.

Tips on body paragraph writing:

• Each paragraph should only deal with one argument, to keep from being cluttered.
• Each paragraph should have a topic sentence to introduce it, and a summary sentence at the end of both wind things up, and lead into the next sentence.
• Each paragraph should reference the thesis statement in some way.
• Each paragraph should fit into the essay in a way which makes it flow properly, leading readers through the essay to a similar conclusion.
• Each paragraph should contain just the right amount of research – not so much as to confuse the issue, but not so little that it seems like there is nothing to say.

Below is an example of the body paragraphs for advertising analysis.

1st paragraph

The commercial appeals to women more than men. This is important because it does not rely on the attractiveness of the model and the setting, but on sensational, emotional responses presenting how perfect men should translate into the reality the ideal image of who a man should be and what he should smell like to attract a wider audience.

2nd paragraph

The advertisement uses an attractive man who seems to be physically fit, giving the product an image that men are appealing to women’s tastes. The advertisement also presents the notion that a man’s’ emotional needs to smell like a real man to attract a woman. The advertisement uses a reliable strategy of sexuality. Sexually themed advertisements appeal to not only men and women but to a wider audience. Using such themes is the surest way to attract more people to use the product.

3rd paragraph

Normally, these advertisements focus on men who are physically attractive to try and sell their products, with the implication that the product will give an entire lifestyle, not simply a way to smell good. This is one way in which the advertisements appeal to people – making it seem as though they too can aspire to be as ‘cool’ as the man presents, simply by purchasing the aforementioned product.

How to write a conclusion

After review, the advertisement giving appropriate evidence to support the claim the next step of the analysis is to wrap up by reviewing the key points of the analysis. The conclusion of the analysis should be a brief summary justifying if the advertisement has achieved its objectives.

Tips to remember when writing your conclusion

• Remember to restate the thesis statement.
• Round up the arguments made in the essay – do not make any original arguments in the conclusion.
• The conclusion is your last chance to bring people round to your point of view, so make it count.
• Remember that you can bring in the history or additional information which is used in the introduction, to remind people of anything that might be useful.
• Your conclusion should mention every argument made in the essay.

Example of a conclusion is shown below.

The Old Spice ad is successful because it makes a good impression on people and makes the audience believe that smelling good can be attractive. The advertisement carefully uses sex appeal, making it attractive for both men and women. Mixing the right amount of humor makes it stand out because of its no offensive. Old Spice’s appeal to women makes men want to look and smells like a real man. The advertisement presents an ideal man as good looking, masculine and romantic. Any advertisement that arouses people’s emotions and people want to watch and remember their products can be termed as a successful advertisement.

Research paper revision

Revision is important since it gives you the opportunity to create the best essay you are capable of. Revision lets you check whether or not your essay flows correctly, whether it makes sense, as well as the smaller things like grammar and punctuation.

• Do two revisions – one for spelling and grammar, and one for structure.
• Check to make sure that the argument through the paper flows correctly.
• Try and come to revision with fresh eyes, since this will help you see problems more easily.
• If you can, ask someone else to read your essay, to point out any errors.
• Make sure to specifically check things like thesis statements, topic sentences, etc.

Need a custom essay?

1.How to write an analysis essay on an advertisement? To analyze an advertisement, one needs first to figure out the objectives behind the Ad film. Then, the analysis will deal with weighting the theme of the Ad and how well it conveyed the message. However, several other aspects are also mentioned in an ad analysis. Discuss the brand’s values and beliefs? Elaborate on the Ad appeal, emotional or rational? Discuss the storyline, the big idea, overall execution of the Ad film.

2.Who can write an analysis essay on an advertisement? Advertisement analysis is best written by field experts available on customessayorder.com. The platform provides wiring help to students who face difficulty in completing their college assignments. The writing company is good with deadlines, free revisions, professional proofreading, and guaranteed high-quality paper delivered on time written by native English speakers.

3.How to conclude an analysis essay on an advertisement? The conclusion simply summarizes the objectives the ad aimed at and how well it conveyed the message to the audience. Mention both the wins and losses. Also, give a sneak preview of how well the persuasion appeal worked for the brand in the ad.

4.What should an analysis essay on an advertisement include? Ad Analysis should identify the rhetorical appeals—logos, pathos, and ethos in the ad. Analyze the ad’s target demography. Moreover, several points to be included in an advertisement analysis are: · The big idea · Type of advertisement campaign – thematic or tactical · Persuasion appeal – emotional or rational · Core brand values · Subliminal message · Testimonial · Production value · budgets · Cast · Locations

How to Write Good Essays on Advertisement

Writing essays is a sincere opportunity for writers to expose their prowess. While in college, the greatest worry for communication students is how to write good essays on advertisement. If you are among those wondering how to write good advertisement analysis essays, we have your back.

Now, essay writing is a gradual process. Mastery of excellent writing skills come with practice. An essay writing website like Gradecrest.com has writers who are masters in essay typing. To become one, you must possess excellent essay writing techniques.

Reading about how to write good essays on advertisements can mold you into a Jedi essay writer. You actually don’t need an advertisement analysis essay example. Instead, knowing what to put where really matters.  With our skills, you can handle both complex and simple essays on TV advertisements.

These insights are from our best writers . Yes, the ones we task with writing rhetorical analysis essays on commercials.  We are sure that in the end, you will become the best essay writer there is in your class. Basically, it is all about ad analysis essays. From how to criticize an advertisement to handling argumentative advertisement essays, we have you.

Let us begin with the outline.

Example of an Advertising Analysis Essay Outline

Before writing an essay on an advertisement, it is always important to draft an outline. Here is a sample:

Introduction

• The name of the advertisement and its purpose. Include the authors and the brand.
• Summary of the context of the ad.
• Background information about the company in the advertisement.
• Your thesis statement.
• The impact of the ad on the intended audience.

Body Paragraph

• Evidence on the effectiveness of the advertisement on the target audience
• Use examples, where applicable.
• Describe the components of the advertisement
• Explore the strategies employed by the advertisers
• The impact of the ad on the audience
• Ethos, pathos, and logos of the ad.
• Textual and visual strategies in the advertisement.
• If it is a comparison, explore the differences and similarities.
• Reinvent the thesis of the essay on advertisement
• Address what makes the advertisement tick
• What was the intention of the ad?
• Reflect on the ad and give your opinion.

Common Topics for Essays on Advertising

• Analysis of Shampoo advertisement
• Pater Philippe advertisement analysis
• Old spice ad analysis essay
• Marketing ad analysis
• Sports marketing ad analysis
• Print ad analysis
• Critical evaluation of an ad
• Automotive ad analysis
• Pepsi advertisement analysis
• McDonalds ad analysis essay
• Victoria Secret ad analysis essay
• Analyzing the strategies used by Victoria Secret advertisements
• Dove ad analysis essay
• Pepsi Halloween Ad analysis
• Coca-Cola ad analysis essay
• Cover girl ad analysis essay
• Nike ad analysis essay
• Power of advertising in today’s world
• History of advertising essay
• Advertisement campaign analysis
• The evolution of advertisement

These are just but a few ad analysis topic ideas. The advertisement analysis essay prompt always has instructions. Sometimes, it is possible to come up with a list of argumentative advertisement analysis essay topics. Be sure to choose what fits the context and instructions.

Advertising Essay Introduction

When writing an introduction about advertising in essays, it is important to figure out what the ad is about. An essay writer always uses a hook and a good thesis statement to spice up their advertisement essay introduction.

Your introduction should also feature some history of the brand, the author of the ad, and where it was aired. Sometimes, it is good to go as deep as the media where it was first aired and at what time. Explore the audience of the advertisement as well, in the introduction.

Here is an example of an advertising essay introduction.

The old spice advertisement uses persuasion strategies such as emotional appeal and logical appeal to convince the audience to buy the product. The advertisement targets the attention of men through women. To grab the attention of the audience, it spices up the message and wraps it in a sexually themed strategy. As such, the old spice advert is one of its kind.

When writing the introduction ensure that you have a thesis statement. Besides, you should avoid deviating into matters, not in the essay.

Still, summarize the major arguments. The example above shows how to start an advertisement analysis essay.

Advertising Essay Conclusion

When writing an argumentative essay on advertising or just an advertisement analysis essay, how you write the conclusion matters.

If you do not get the paragraphs right, you will be asking how to add more to an ad analysis essay. Let us see how it is done.

When writing the essay conclusion, restate the thesis , not as it is in the introduction but in a reinvented format.

Sum up the arguments in the body paragraphs and use the words that denote a closing sentence. Your conclusion should make clear what your argument in the paper is. Reflect on how the advertisement was successful or how it failed.

Choosing Proper Words for the Essay

When writing, a good choice of words exposes your intelligence. As such, always ensure that your essay flows, is coherent, and is relevant to the topic.

Using language effectively can help build sound arguments and capture the main ideas in the ad.

We advise students to make it simple. If you must use a synonym, maybe to avoid plagiarism when paraphrasing, ensure it fits the context.

For logical flow, use some of the transition words and phrases.

Words such as moreover, besides, for example, furthermore, and however, to mention a few, should feature in often in your essay.

Quoting from other Sources

If there is one thing that makes writing sweet, it is using the right referencing skills. An essay is a chance for you to showcase your essay writing skills.

Thus, you should ensure that you are quoting opinions from other people or even copyrighted material to support ideas.

While using quotes from other people show the extent to which you did your research, be sure to use in-text citation and a reference list.

When writing about the history of the United States of America, you should borrow ideas from relevant materials written by authors on the same theme.

The same applies to when analyzing an advertisement about McDonald’s. You must find materials that talk about persuasive strategies in an advertisement, impacts of McDonald’s advertisements, and any relevant literature. if you master this, there is no need to worry about how to write good essays on advertisements.

Using Proper Vocabulary in an Essay on Advertisement

If there are one place writers err, it is the use of vocabulary. An essay written with the right vocabulary flows. It takes quite some patience and practice to master the use of vocabulary.

When you use rambling words in your essay, make sure you use lower cases to capture the attention of the readers.

You can also achieve modest clarity in your essay by using the right vocabulary. However, to avoid the trap of just throwing in words, always do proper research.

You can only express your ideas with clarity when you understand how the vocabularies fit.

The internet is fraught of learning avenues for one to master vocabulary. Use Online dictionaries such as Merriam Webster or Oxford Dictionary.

Also, you can use the thesaurus to master how to place and use vocabulary in your text.

Essay Tone and Good Grammar Equals Good Grades

A good essay writer maintains a good essay tone from start to finish. Talk about sentence structures; they count here.

A good tone makes it easy for readers. When writing advertising essays always arrange the points in a logical manner.

Avoid grammar errors at all cost when writing the essay. As soon as you are down, proofread the paper to correct any grammatical errors present.

Also, ensure that the punctuation use in your essay is excellent. Submit an essay on an advertisement that you are sure will bring great grades.

Get Help if you wish to

It takes time to develop good essay writing. With this article on how to write good essays on an advertisement, we explore every detail you need to know when writing ad analysis essays.

To recap, your introductory sentence has to present the concepts of the advertisement you are analyzing in the essay.

The body paragraphs should have the best sentence structures, exhibit good vocabulary use, be devoid of grammar errors, and develop the thesis in the introduction of your advertisement essay.

Your conclusion should summarize and restate the thesis statement.

Well, it is also possible that writing an advertising essay is not your thing! In this case, you need to hire essay writers to act on your paper. Gradecrest.com has the best essay writers to help with your essays. We can handle essays as urgent as the 3-hour deadline .

When you order from us, you get a paper that factors in all the parameters discussed in the article.

Gradecrest is a professional writing service that provides original model papers. We offer personalized services along with research materials for assistance purposes only. All the materials from our website should be used with proper references. See our Terms of Use Page for proper details.

Unlocking the Masterpiece Within: The Art of Pro

How To Analyze An Advertisement

How to analyze an advertisement.

In the fast-paced world of marketing and advertising, understanding how to analyze an advertisement is a crucial skill. Whether you’re a marketing professional, a business owner, or simply a curious consumer, dissecting ads can provide valuable insights into their effectiveness, messaging, and target audience. In this comprehensive guide, we’ll delve into the strategies and techniques that will empower you to decode advertisements like a pro.

The Importance of Advertisement Analysis

Understanding the basics.

Before diving into the intricacies of ad analysis, let’s establish a solid foundation. Ads come in various forms, including print, digital, television, and social media. They are designed with a specific purpose: to convey a message and persuade the audience. Your ability to dissect an ad hinges on grasping these fundamental concepts.

Deconstructing Visual Elements

Ads are a visual medium, and their design elements are carefully crafted to capture attention. Look for eye-catching colors, images, and typography. Ask yourself how these elements contribute to the ad’s overall message. Visual analysis can provide insights into the emotions and associations the ad aims to evoke.

Deciphering the Message

Every ad has a core message or theme. Analyze the ad’s copy (text) and imagery to identify this message. Consider the tone, language, and symbolism used. What emotions does the ad try to evoke? Does it address a problem and offer a solution? Understanding the central message is key to evaluating an ad’s effectiveness.

Digging Deeper into Advertisement Analysis

Target audience analysis.

Ads are tailored to specific demographics. To analyze an ad effectively, you must identify the intended audience. Consider age, gender, interests, and values. The more you can pinpoint the target audience, the better you can evaluate whether the ad resonates with them.

Evaluating Persuasion Techniques

Ads often employ various persuasion techniques to influence consumers. These can include appeals to emotion, logic, or authority. Analyze which methods the ad uses and how effectively they are executed. Does the ad create a compelling argument or emotional connection?

Assessing Call to Action (CTA)

A successful ad should prompt action. Analyze the ad’s call to action, such as “buy now,” “subscribe,” or “learn more.” Is the CTA clear and compelling? Does it create a sense of urgency? Assessing the CTA can help determine the ad’s ability to drive desired behaviors.

Measuring Impact and Effectiveness

Tracking results.

After an ad campaign, it’s essential to measure its impact. Look at metrics like website traffic, conversion rates, and sales figures. Did the ad achieve its intended goals? By evaluating real-world results, you can gauge an ad’s effectiveness.

Comparing to Competitors

To gain a competitive edge, compare the analyzed ad to those of competitors. How does it stack up in terms of creativity, messaging, and audience engagement? Analyzing the competition can inspire improvements in future campaigns.

Evolving Your Analytical Skills

Advertisement analysis is an ongoing process. Continuously hone your skills by studying a variety of ads, staying updated on industry trends, and seeking feedback from peers or mentors. Over time, you’ll become a seasoned ad analyst, capable of deciphering the most complex advertising strategies.

In conclusion, the ability to analyze an advertisement is a valuable skill in today’s advertising-saturated world. By understanding the basics, deconstructing visual elements, deciphering the message, and delving into deeper analysis, you can assess the effectiveness of advertisements with precision. Remember that practice makes perfect, so keep analyzing ads to refine your skills and stay ahead in the ever-evolving field of advertising.

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“Open that Coca-Cola”. Advertisement Analysis Essay (Critical Writing)

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

I found the “Open that Coca-Cola” advertisement on the company’s YouTube page. The video is marketing the Coca-Cola soda since all the participants are taking the product. It presents a scenario where individuals engaging in different activities like watching soccer, shopping in a store, and having dinner with family members become energized immediately after sipping the beverage (Coca-Cola, 2021). For example, a young man at a supermarket grabs a bottle from the refrigerator, drinks the soda, and starts dancing energetically.

His two other friends also purchase the same drink, and they walk away from the store to the street excitedly. In another incident, two young ladies are playing a video game in a room. After several seconds, they seem refreshed and extremely energized as they leave the game and start bopping to the music.

This advertisement targets athletes, students, singers, and other youths between the age of fourteen and twenty-five years. I know this because almost every character involved in this video belongs to that age group. Additionally, the ad includes activities that millennials are more likely to participate in their lives. For example, the three young men are dancing on the street while another lady and a gentleman are watching a skincare routine on a laptop with face masks on and in robes (Coca-Cola, 2021). Using activities members of the target audience appeals to them and makes it easier for Coca-Cola Company to influence their purchasing behavior. The happiness explosion makes the viewers believe that they will become cheerful by taking the beverage.

This advertisement’s principal claim is that Coca-Cola soda triggers pleasure and energy instantly. It also suggests that sipping the beverage brings people more happiness. All the young men and women on the video burst in joy and start dancing immediately after sipping from the bottle (Coca-Cola, 2021).

This claim is not credible because, realistically, the beverage can only begin to functioning after minutes or hours. Another reason why the claim might not be sincere is because a simple beverage cannot be the primary source of excitement in people’s lives. Coca-Cola Company can make the ad’s claim more credible by making the ad more authentic and realistic. For example, the character should feel happier and more energetic in the activities they are engaging instead of dancing because they have taken the drink. The company can make the claim less credible by using dull colors and less humor. It would be difficult to convince people that they will be happy if the ad used a dull background and designs.

The Coca-Cola advert uses various rhetoric techniques to sell the product to the target audience. Firstly, the creator uses ethos by including familiar models like family or friends’ meetings. This technique revives the concept of life values and makes the audience interested to purchase the product. It captures the attention of those who love to watch sports or play video games with their friends and have dinner together with their parents or siblings.

The commercial also implements pathos by convincing the consumers that taking the beverage will give them happiness and pleasure. The characters are smiling and dancing regardless of their setting or activities. The video reveals the loss aversion cognitive bias. It does not show the three men at the beginning of the video paying for the product. They leave the store attendant surprised as they walk out of the store dancing and drinking the soda. This scenario demonstrates that consumers can engage with the merchandise without monetary loss.

Coca-Cola. (2021). Open That Coca-Cola . Web.

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IvyPanda. (2022, October 18). “Open that Coca-Cola”. Advertisement Analysis. https://ivypanda.com/essays/open-that-coca-cola-advertisement-analysis/

"“Open that Coca-Cola”. Advertisement Analysis." IvyPanda , 18 Oct. 2022, ivypanda.com/essays/open-that-coca-cola-advertisement-analysis/.

IvyPanda . (2022) '“Open that Coca-Cola”. Advertisement Analysis'. 18 October.

IvyPanda . 2022. "“Open that Coca-Cola”. Advertisement Analysis." October 18, 2022. https://ivypanda.com/essays/open-that-coca-cola-advertisement-analysis/.

1. IvyPanda . "“Open that Coca-Cola”. Advertisement Analysis." October 18, 2022. https://ivypanda.com/essays/open-that-coca-cola-advertisement-analysis/.

Bibliography

IvyPanda . "“Open that Coca-Cola”. Advertisement Analysis." October 18, 2022. https://ivypanda.com/essays/open-that-coca-cola-advertisement-analysis/.

Home — Essay Samples — Business — Consumer Behavior — Skittles: Ad Analysis

Skittles: AD Analysis

• Categories: Advertisement Aesthetics Consumer Behavior

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Words: 723 |

Published: Mar 16, 2024

Words: 723 | Pages: 2 | 4 min read

Table of contents

Visual and aesthetic appeal, emotional connection, brand identity.

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How to write an Advertisement Analysis for MBA

• March 7, 2023
• Study Guides

Understanding Advertisement Analysis

Advertisement analysis is critically examining and evaluating advertisements better to understand their content, meaning, and impact. This process involves analyzing an advertisement’s text, visuals, and overall design and considering the target audience and the context in which the ad appears. By conducting an advertisement analysis , businesses can gain valuable insights into the effectiveness of their advertising campaigns and make informed decisions about how to improve them.

Here's What You'll Learn

Techniques for Advertisement Analysis

Several techniques can be used to conduct a practical advertisement analysis:

1. Text Analysis

Text analysis involves examining the language and messaging used in an advertisement to understand its intended meaning and impact on the audience . This includes analyzing the ad’s headline, tagline, copy, and other written content.

a. Headline Analysis

The headline is often the first thing a viewer sees in an advertisement, and it plays a crucial role in capturing their attention and interest. Analyzing the headline can reveal necessary information about the ad’s purpose and target audience.

b. Language Analysis

The language used in an advertisement can convey various messages and emotions and can be analyzed to determine how the ad is trying to influence the audience. This includes examining the ad’s written content’s tone, style, and vocabulary.

c. Persuasive Techniques Analysis

Many advertisements use persuasive techniques to influence the audience’s beliefs or behaviors. These techniques include appeals to emotion, authority, and social proof, and they can be analyzed to understand how the ad is trying to persuade the viewer.

2. Visual Analysis

The visual analysis involves examining an advertisement’s design, layout, and overall visual elements to understand how they contribute to its message and impact on the audience.

a. Layout Analysis

The layout of an advertisement can be analyzed to understand how it guides the viewer’s attention and emphasizes some aspects of the ad. This includes examining the placement and size of visual elements and text.

b. Design Analysis

The design elements of an advertisement, such as color, font, and imagery, can be analyzed to understand how they contribute to the ad’s overall message and impact on the viewer.

c. Color Analysis

The colors used in an advertisement can convey various emotions and messages, and they can be analyzed to understand how the ad is trying to influence the viewer.

Importance of Advertisement Analysis

Advertisement analysis is an essential process for businesses for several reasons:

1. Understanding Consumer Behavior

By analyzing advertisements, businesses can gain a deeper understanding of consumer behavior, including motivations, needs, and decision-making processes . This information can be used to develop more effective marketing campaigns and better meet the needs of their target audience.

a. Analyzing Motivations

Analyzing consumer behavior motivations can help businesses better understand what drives their target audience to purchase.

b. Identifying Consumer Needs

By understanding the needs of their target audience, businesses can develop more effective advertising campaigns that address those needs and solve their problems.

c. Understanding the Decision-Making Process

Understanding their target audience’s decision-making process can help businesses create more persuasive advertising campaigns that appeal to their interests and preferences.

2. Competitive Advantage

By analyzing advertisements, businesses can gain valuable insights into their competitors’ marketing strategies and identify gaps in the market that they can exploit. This information can be used to develop unique selling propositions that differentiate their products or services from their competitors.

Examples of Advertisement Analysis

1. coca-cola’s “share a coke” campaign.

Coca-Cola’s “Share a Coke” campaign was hugely successful in multiple countries worldwide. The campaign’s main concept was to personalize Coke bottles with popular names, encouraging customers to share their Coke bottles with friends and family. Here’s an analysis of the campaign:

 a. Text Analysis

The campaign’s “Share a Coke” tagline is short, memorable, and easy to understand. The use of personalized names on the Coke bottles made the campaign feel more personal, and the tagline encouraged people to share their Coke bottles, thus creating a sense of community.

b. Visual Analysis

The visual elements of the campaign were simple yet effective. The personalized Coke bottles with different names were eye-catching, and the images of people sharing their bottles were relatable and emotional.

c. Audience Analysis

The campaign’s target audience was young people aged 18-34 who were active on social media platforms . The campaign encouraged customers to share their personalized Coke bottles on social media using a specific hashtag, which helped to spread the campaign’s message.

 2. Nike’s “Just Do It” Campaign

Nike’s “Just Do It” campaign is one of the most iconic and successful ad campaigns ever. The campaign’s tagline has become synonymous with Nike and its brand message. Here’s an analysis of the campaign:

The campaign’s “Just Do It” tagline is short, memorable, and inspiring. It’s a powerful call to action, encouraging people to push themselves to achieve their goals .

The campaign’s visual elements were also powerful. Using black and white images with the Nike logo and tagline in bold letters was simple yet effective.

c. Brand Analysis

The “Just Do It” campaign helped position Nike as a brand about pushing boundaries and challenging oneself. The campaign’s message resonated with customers who wanted to feel empowered and motivated.

 Best Advertisement Analysis Tools

 1. google adwords.

Google AdWords is a powerful tool businesses can use to create and manage online advertising campaigns . Here are some of the features that make Google AdWords a great tool for advertisement analysis:

a. Keyword Planner

Keyword Planner helps businesses to find the right keywords for their advertising campaigns. It provides keyword search volume, competition, and cost-per-click data.

b. Display Planner

Display Planner helps businesses to create effective display ads by providing data on audience demographics, interests, and behaviors.

c. Ad Preview and Diagnosis

Ad Preview and Diagnosis help businesses preview their ads and diagnose any issues that might prevent them from appearing on Google search results.

SEMrush is an all-in-one marketing tool that provides businesses with valuable insights into their competitors’ advertising strategies . Here are some of the features that make SEMrush a great tool for advertisement analysis:

a. Advertising Research

Advertising Research provides data on competitors’ advertising strategies, including their ad copy, targeting, and display networks.

 b. Ad Builder

Ad Builder helps businesses to create effective display ads by providing templates, design tools, and targeting options.

AdSense helps businesses to monetize their websites by displaying targeted ads. It provides data on ad performance, revenue, and ad networks.

How to Conduct Effective Advertisement Analysis

Effective advertisement analysis involves several key steps:

1. Establishing the Purpose

To conduct an effective advertisement analysis, it is important first to establish the purpose . This involves identifying the ad’s objective, determining the target audience, and identifying the ad’s call to action.

a. Identifying the Ad’s Objective

This involves understanding what the ad is trying to achieve, such as increasing sales or building brand awareness .

b. Determining the Target Audience

It is essential to identify the intended audience for the ad to tailor the analysis accordingly.

c. Identifying the Ad’s Call to Action

The analysis should consider the ad’s call to action and evaluate its effectiveness in prompting the desired response from the audience.

 2. Gathering Information

The second step involves gathering information about the ad, including analyzing its text and visuals, researching its background, and understanding its context.

a. Analyzing the Ad’s Text and Visuals

This involves examining the ad’s language, images, and other visual elements to determine how effectively they convey the intended message.

b. Researching the Ad’s Background

Researching the ad’s background can provide insights into the target audience , the brand’s messaging, and placement.

c. Understanding the Ad’s Context

Understanding the ad’s context involves considering the cultural, social, and political factors that may impact the ad’s effectiveness.

3. Evaluation and Conclusion

The final step is to evaluate the ad’s effectiveness, provide recommendations, and summarize the analysis.

a. Assessing the Ad’s Effectiveness

This involves measuring the ad’s success in achieving its objectives and determining its impact on the target audience.

b. Providing Recommendations

Based on the analysis, recommendations can be made to improve the ad’s effectiveness or to inform future ad campaigns .

c. Summarizing the Analysis

The analysis should clearly and concisely summarize key insights and findings.

How do you analyze an advertisement?

To analyze an advertisement, you need to identify the ad’s objective , determine the target audience, analyze the ad’s text and visuals, research the ad’s background and context, and evaluate the ad’s effectiveness.

What are the five parts of an advertisement?

The five parts of an advertisement are the headline, subheadline, body copy, visual, and call to action.

What are the four elements of a successful advertisement?

The four elements of a successful advertisement are attention, interest, desire, and action. A successful ad captures the audience’s attention, generates interest, creates a desire for the product or service, and includes a clear call to action.

How do you write a good analysis?

To write a good analysis, you should understand the purpose of the analysis and the audience for which it is intended. You should then gather relevant information, organize your thoughts, and provide clear, concise, and logical explanations. Your analysis should be supported by evidence and examples and include improvement recommendations.

What are the eight advertising techniques?

The eight advertising techniques are emotional appeals, testimonials, endorsements, bandwagon, fear appeals, humor, sex appeals, and plain folks. These techniques persuade the audience to buy a product or service by appealing to their emotions, desires, fears, or values.

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Advertisement Analysis Essay: Writing Tips

Let's first define the analysis essay to understand what it is. Analysis essays imply examination and evaluation of a particular work like books, newspapers, journals, articles or advertisements. No matter what you analyze, your purpose is going to be the same:

• break your subject into components;
• examine each part separately;
• find the connection between those parts.

For instance, if you are assigned to analyze a poem, you will have to find a relation between the content of the poem and its form. If you have to interpret a play, you might need to find a link between the plots and subplots, and follow the character development by discussing their acting during the performance. There might be different goals when it comes to analysis. It's always important to understand clearly what your professor wants you to highlight.

Ad analysis essay is aimed to study a particular advertisement, provide its main points and give your opinion on its impact on the audience. Advertising has played and continues to play a tremendous role in our lives. We face it everywhere: television, the Internet, roads, shops etc. It doesn't matter whether the advertisement is aimed to sell the product or raise the awareness of the audience about something - it's still has a powerful influence. Therefore, it's imperative to analyze advertisements and understand how they work. If you are wondering how to write an ad analysis essay which can impress the readers, then you came to the right place! Check out the guideline below and write an eye-catching ad analysis essay or get custom online essays from professional writers.

Ad analysis essay guidelines for students

Just as every kind of academic writing, an ad analysis essay has a standard structure which should be strictly followed. Before we start discussing this basic structure and its component, we want to give a list of questions related to the advertisement, which you should work on before writing the essay.

• First of all, make an introduction to the subject which is advertised. Your readers might not be familiar with the service or the product advertised in your case. The earlier you introduce the advertisement, the easier it is to comprehend. No matter what your ad discusses or how popular it is - give a small description for everyone to have a clear understanding of what they are going to read in your essay.
• You should also try to understand what "the audience" is. You should realize who you are going to work with because this will help you focus on the right things and highlight those aspects which are interesting for your readers.
• It's also critical to understand the purpose of the advertisement and why you write the essay on this ad. Why are you telling your readers about the mechanism of this particular advertisement? A clear understanding of the purpose will let you write a well-structured paper.
• Another thing you should pay attention to is the thesis. It's an overall point which you discuss in the rest of the essay.
• Take some time to organize your task. There should be a certain order of the things you want to present in your analysis, and you should come up with this sequence before writing.

Your analysis essay should be simple and challenging at same time. Of course, it tries to show what the creator of the advertisement wanted to convey to everyone but you should also help the reader realize all the positive and negative influences of this advertisement. In most cases, the executives try to sell their products to as many people as possible. They might spend fortunes on commercials. The psychological techniques used to convince people are very intricate because they influence our way of thinking subliminally. They alter our preferences and make us buy things we would have never bought. Your readers should get a broader picture of the advertisement and be aware of all the pitfalls it poses. In short, you should describe how effective the ad is or was.

Every essay writing dilemma can be solved right now!

Useful structure tips, and topic examples

We offer a basic structure you can apply while writing an analysis essay. If you want to write a high-quality advertising analysis essay - just follow these simple steps:

• Come up with the title and thesis statement.
• Write the introduction. The introduction aims to capture your readers' attention. As it has been stated before, you should give some background information relevant to your advertisement and indicate your opinion on it to show the position you are taking. In this part of the essay, you should include your thesis statement and description of the topic.
• The body part of the essay. In this section, you lay out the main paragraphs (at least 3 paragraphs for a 500-700-words essay) which support your thesis. Provide the evidence, facts and examples. This will assure the reader that your viewpoint is backed by solid proof. You can use textual evidence which includes a summary, paraphrasing, specific details and quotations. Try to take as much information from the advertisement as you can. Don't miss any details and discuss every single aspect of the ad.
• Conclusion for an advertisement analysis essay. It is the culmination of your whole work. You should summarize all main points and give your final comment about the ad.

Create an advertisement analysis essay outline. Many people skip this part despite the fact that it helps the author organize all their ideas and thoughts. When it comes to outline writing, you should mention what your topic is, why it caught your attention and what your opinion is. What is more, you should include short names for all paragraphs of the essay and a brief description of what you are going to write in each of them.

Take your time to choose the most suitable topic for your advertisement analysis essay. Select what is interesting for both you and the audience. Here are some examples of ad analysis essay topics:

• What is the message behind the Burger King's advertisement "BK Super Seven Incher"?
• Does the new Coca-Cola commercial convince people that they are going to "Open Happiness"?
• Does Bud Light's "drinkability" have "viability"?
• How the military commercials influence our mind

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Nike Advertisement Analysis Essay Sample

Nike is an international company that has been around for almost 50 years. Get ready for a journey through the world of marketing mastery with a compelling analysis of a Nike advertisement. This essay delves into the art behind Nike’s campaign, offering students a captivating example of advertising brilliance. Beyond the swoosh and catchy slogans lies a blueprint for effective communication, inspiring students to decode the language of impactful marketing.

Join me in exploring the nuances that transform a mere advertisement into a powerful narrative. This analysis is not just about shoes; it’s a concise lesson for students navigating academia and beyond – a stepping stone to your own marketing triumphs.

Essay Example On Nike Advertisement Analysis

• Introduction of Nike Advertisement Analysis Essay
• Analysis of Nike Advertisement’s Visual Impact
• Analysis on Emotional Resonance of Nike Advertisement
• Slogans and Taglines of Nike Advertisement
• Analysis of Celebrity Endorsements
• Cultural Relevance of Nike Advertisement to do Analysis

Introduction of Nike Advertisement Analysis Essay Nike, a marketing powerhouse, wields the iconic swoosh symbol and creates compelling campaigns that transcend simple product promotion, establishing dominance. In this essay, we’ll analyze a Nike ad, exploring visual and textual elements to reveal persuasive techniques employed in its creation. Embark on a Nike advertising odyssey, where every frame and word holds a purpose, weaving a narrative beyond the surface. Body of Essay Sample on Nike Advertisement Analysis Analysis of  Nike Advertisement’s Visual Impact   Nike advertisements are synonymous with visually striking imagery. From empowering shots of athletes in action to emotionally charged scenes, each frame is meticulously crafted to evoke a response. Our analysis will explore how these visuals contribute to the overall narrative, leaving an indelible mark on the viewer’s psyche. Get Non-Plagiarized Custom Essay on Nike Advertisement Analysis in USA Order Now Analysis on Emotional Resonance of Nike Advertisement Beyond the glossy visuals, Nike advertisements often tap into the realm of human emotion. Whether it’s the triumph of overcoming adversity or the joy of personal achievement, we’ll explore how these emotional triggers are strategically integrated to establish a connection with the audience. Slogans and Taglines of Nike Advertisement  Nike’s taglines are short yet impactful, leaving a lasting impression on consumers. From the iconic “Just Do It” to newer campaigns, our analysis will dissect the linguistic choices made in these slogans and unveil the psychological impact they have on the audience. Read more:- Argumentative Essay Topics About Animals Analysis of Celebrity Endorsements  Nike frequently collaborates with high-profile athletes, turning them into brand ambassadors. We’ll examine the symbiotic relationship between the brand and these influencers, exploring how their association adds credibility and authenticity to Nike’s messaging. Buy Customized Essay on Nike Advertisement Analysis At Cheapest Price Order Now Cultural Relevance of Nike Advertisement to do Analysis  Nike has a knack for staying culturally relevant, addressing societal issues and movements. Our essay will discuss how these strategic moves not only reflect the brand’s social responsibility but also position Nike as a leader in cultural conversations. Conclusion In the final segment of our analysis, we will tie together the various elements explored in the Nike advertisement. From visual impact to emotional resonance, slogans to celebrity endorsements, we’ll paint a comprehensive picture of how Nike’s advertising strategies captivate and engage audiences worldwide. As we dissect the intricacies of a Nike advertisement, it becomes evident that every element is a calculated step towards brand supremacy. Marketers and enthusiasts can glean insights on impactful campaigns by understanding persuasive techniques, enhancing the creation of memorable content. Hire USA Experts for Nike Advertisement Analysis Essay Order Now

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• How to write a rhetorical analysis | Key concepts & examples

How to Write a Rhetorical Analysis | Key Concepts & Examples

Published on August 28, 2020 by Jack Caulfield . Revised on July 23, 2023.

A rhetorical analysis is a type of essay  that looks at a text in terms of rhetoric. This means it is less concerned with what the author is saying than with how they say it: their goals, techniques, and appeals to the audience.

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Table of contents

Key concepts in rhetoric, analyzing the text, introducing your rhetorical analysis, the body: doing the analysis, concluding a rhetorical analysis, other interesting articles, frequently asked questions about rhetorical analysis.

Rhetoric, the art of effective speaking and writing, is a subject that trains you to look at texts, arguments and speeches in terms of how they are designed to persuade the audience. This section introduces a few of the key concepts of this field.

Appeals: Logos, ethos, pathos

Appeals are how the author convinces their audience. Three central appeals are discussed in rhetoric, established by the philosopher Aristotle and sometimes called the rhetorical triangle: logos, ethos, and pathos.

Logos , or the logical appeal, refers to the use of reasoned argument to persuade. This is the dominant approach in academic writing , where arguments are built up using reasoning and evidence.

Ethos , or the ethical appeal, involves the author presenting themselves as an authority on their subject. For example, someone making a moral argument might highlight their own morally admirable behavior; someone speaking about a technical subject might present themselves as an expert by mentioning their qualifications.

Pathos , or the pathetic appeal, evokes the audience’s emotions. This might involve speaking in a passionate way, employing vivid imagery, or trying to provoke anger, sympathy, or any other emotional response in the audience.

These three appeals are all treated as integral parts of rhetoric, and a given author may combine all three of them to convince their audience.

Text and context

In rhetoric, a text is not necessarily a piece of writing (though it may be this). A text is whatever piece of communication you are analyzing. This could be, for example, a speech, an advertisement, or a satirical image.

In these cases, your analysis would focus on more than just language—you might look at visual or sonic elements of the text too.

The context is everything surrounding the text: Who is the author (or speaker, designer, etc.)? Who is their (intended or actual) audience? When and where was the text produced, and for what purpose?

Looking at the context can help to inform your rhetorical analysis. For example, Martin Luther King, Jr.’s “I Have a Dream” speech has universal power, but the context of the civil rights movement is an important part of understanding why.

Claims, supports, and warrants

A piece of rhetoric is always making some sort of argument, whether it’s a very clearly defined and logical one (e.g. in a philosophy essay) or one that the reader has to infer (e.g. in a satirical article). These arguments are built up with claims, supports, and warrants.

A claim is the fact or idea the author wants to convince the reader of. An argument might center on a single claim, or be built up out of many. Claims are usually explicitly stated, but they may also just be implied in some kinds of text.

The author uses supports to back up each claim they make. These might range from hard evidence to emotional appeals—anything that is used to convince the reader to accept a claim.

The warrant is the logic or assumption that connects a support with a claim. Outside of quite formal argumentation, the warrant is often unstated—the author assumes their audience will understand the connection without it. But that doesn’t mean you can’t still explore the implicit warrant in these cases.

For example, look at the following statement:

We can see a claim and a support here, but the warrant is implicit. Here, the warrant is the assumption that more likeable candidates would have inspired greater turnout. We might be more or less convinced by the argument depending on whether we think this is a fair assumption.

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Rhetorical analysis isn’t a matter of choosing concepts in advance and applying them to a text. Instead, it starts with looking at the text in detail and asking the appropriate questions about how it works:

• What is the author’s purpose?
• Do they focus closely on their key claims, or do they discuss various topics?
• What tone do they take—angry or sympathetic? Personal or authoritative? Formal or informal?
• Who seems to be the intended audience? Is this audience likely to be successfully reached and convinced?
• What kinds of evidence are presented?

By asking these questions, you’ll discover the various rhetorical devices the text uses. Don’t feel that you have to cram in every rhetorical term you know—focus on those that are most important to the text.

The following sections show how to write the different parts of a rhetorical analysis.

Like all essays, a rhetorical analysis begins with an introduction . The introduction tells readers what text you’ll be discussing, provides relevant background information, and presents your thesis statement .

Hover over different parts of the example below to see how an introduction works.

Martin Luther King, Jr.’s “I Have a Dream” speech is widely regarded as one of the most important pieces of oratory in American history. Delivered in 1963 to thousands of civil rights activists outside the Lincoln Memorial in Washington, D.C., the speech has come to symbolize the spirit of the civil rights movement and even to function as a major part of the American national myth. This rhetorical analysis argues that King’s assumption of the prophetic voice, amplified by the historic size of his audience, creates a powerful sense of ethos that has retained its inspirational power over the years.

The body of your rhetorical analysis is where you’ll tackle the text directly. It’s often divided into three paragraphs, although it may be more in a longer essay.

Each paragraph should focus on a different element of the text, and they should all contribute to your overall argument for your thesis statement.

Hover over the example to explore how a typical body paragraph is constructed.

King’s speech is infused with prophetic language throughout. Even before the famous “dream” part of the speech, King’s language consistently strikes a prophetic tone. He refers to the Lincoln Memorial as a “hallowed spot” and speaks of rising “from the dark and desolate valley of segregation” to “make justice a reality for all of God’s children.” The assumption of this prophetic voice constitutes the text’s strongest ethical appeal; after linking himself with political figures like Lincoln and the Founding Fathers, King’s ethos adopts a distinctly religious tone, recalling Biblical prophets and preachers of change from across history. This adds significant force to his words; standing before an audience of hundreds of thousands, he states not just what the future should be, but what it will be: “The whirlwinds of revolt will continue to shake the foundations of our nation until the bright day of justice emerges.” This warning is almost apocalyptic in tone, though it concludes with the positive image of the “bright day of justice.” The power of King’s rhetoric thus stems not only from the pathos of his vision of a brighter future, but from the ethos of the prophetic voice he adopts in expressing this vision.

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The conclusion of a rhetorical analysis wraps up the essay by restating the main argument and showing how it has been developed by your analysis. It may also try to link the text, and your analysis of it, with broader concerns.

Explore the example below to get a sense of the conclusion.

It is clear from this analysis that the effectiveness of King’s rhetoric stems less from the pathetic appeal of his utopian “dream” than it does from the ethos he carefully constructs to give force to his statements. By framing contemporary upheavals as part of a prophecy whose fulfillment will result in the better future he imagines, King ensures not only the effectiveness of his words in the moment but their continuing resonance today. Even if we have not yet achieved King’s dream, we cannot deny the role his words played in setting us on the path toward it.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The goal of a rhetorical analysis is to explain the effect a piece of writing or oratory has on its audience, how successful it is, and the devices and appeals it uses to achieve its goals.

Unlike a standard argumentative essay , it’s less about taking a position on the arguments presented, and more about exploring how they are constructed.

The term “text” in a rhetorical analysis essay refers to whatever object you’re analyzing. It’s frequently a piece of writing or a speech, but it doesn’t have to be. For example, you could also treat an advertisement or political cartoon as a text.

Logos appeals to the audience’s reason, building up logical arguments . Ethos appeals to the speaker’s status or authority, making the audience more likely to trust them. Pathos appeals to the emotions, trying to make the audience feel angry or sympathetic, for example.

Collectively, these three appeals are sometimes called the rhetorical triangle . They are central to rhetorical analysis , though a piece of rhetoric might not necessarily use all of them.

In rhetorical analysis , a claim is something the author wants the audience to believe. A support is the evidence or appeal they use to convince the reader to believe the claim. A warrant is the (often implicit) assumption that links the support with the claim.

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Since last May, the average person experienced 26 more days of abnormal warmth than they would have without global warming, a new analysis found.

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Over the past year of record-shattering warmth, the average person on Earth experienced 26 more days of abnormally high temperatures than they otherwise would have, were it not for human-induced climate change, scientists said Tuesday.

The past 12 months have been the planet’s hottest ever measured, and the burning of fossil fuels, which has added huge amounts of heat-trapping gases to the atmosphere, is a major reason. Nearly 80 percent of the world’s population experienced at least 31 days of atypical warmth since last May as a result of human-caused warming, the researchers’ analysis found.

Hypothetically, had we not heated the globe to its current state , the number of unusually warm days would have been far fewer, the scientists estimated, using mathematical modeling of the global climate.

The precise difference varies place to place. In some countries, it is just two or three weeks, the researchers found. In others, including Colombia, Indonesia and Rwanda, the difference is upward of 120 days.

“That’s a lot of toll that we’ve imposed on people,” said one of the researchers who conducted the new analysis, Andrew Pershing, the vice president for science at Climate Central, a nonprofit research and news organization based in Princeton, N.J., adding, “It’s a lot of toll that we’ve imposed on nature.” In parts of South America and Africa, he said, it amounts to “120 days that just wouldn’t be there without climate change.”

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• Published: 08 June 2024

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

• Xiaoyu Liu 1 , 2   na1 ,
• Linhao Qu 1 , 2   na1 ,
• Ziyue Xie 1 , 2 ,
• Jiayue Zhao 1 , 2 ,
• Yonghong Shi 1 , 2 &
• Zhijian Song 1 , 2  

BioMedical Engineering OnLine volume  23 , Article number:  52 ( 2024 ) Cite this article

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Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords “multi-organ segmentation” and “deep learning”, resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

Introduction

Accurate segmentation of multiple organs in medical images is essential for various medical applications such as computer-aided diagnosis, surgical planning, navigation, and radiotherapy treatment [ 1 , 2 ]. For instance, radiation therapy is a common treatment option for cancer patients, where tumor masses and high-risk microscopic areas are targeted [ 3 ]. However, radiation therapy can pose a significant risk to normal organs adjacent to the tumor, which are called organs at risk (OARs). Therefore, precise segmentation of both tumor and OARs contours is necessary to minimize the risk of radiation therapy [ 4 , 5 ].

The early segmentation process relies heavily on manual labeling by physicians, which is labour-intense and time-consuming. For example, mapping 24 OARs in the head and neck region takes over 3 h, resulting in potential long waits for patients, especially in cases of patient overload [ 6 ]. Due to a shortage of experienced doctors, the mapping process becomes even more time-consuming, potentially delaying the patient's treatment process and missing the optimal treatment window [ 7 ]. Furthermore, the labeling results obtained by different physicians or hospitals exhibit significant variability [ 8 , 9 , 10 , 11 ]. Therefore, there is a pressing requirement for accurate and automated multi-organ segmentation methods in clinical practice.

Traditional methods [ 12 , 13 , 14 , 15 ] usually utilize manually extracted image features for image segmentation, such as the threshold method [ 16 ], graph cut method [ 17 ], and region growth method [ 18 ]. Limited by numerous manually extracted image features and the selection of non-robust thresholds or seeds, the segmentation results of these methods are usually unstable, and often yield only a rough segmentation result or only apply to specific organs. Knowledge-based methods leverage labeled datasets to automatically extract detailed anatomical information for various organs, reducing the need for manual feature extraction. This method can enhance the accuracy and robustness of multi-organ segmentation techniques, such as multi-atlas label fusion [ 19 , 20 ] and statistical shape models [ 21 , 22 ]. The method based on multi-atlas uses image alignment to align predefined structural contours to the image to be segmented. But this method typically includes multiple steps, therefore, the performance of this method may be influenced by various relevant factors involved in each step. Moreover, due to the use of fixed atlases, it is challenging to manage the anatomical variation of organs between patients. In addition, it is computationally intensive and takes a long time to complete an alignment task. The statistical shape model uses the positional relationships between different organs, and the shape of each organ in the statistical space as a constraint to regularize the segmentation results. However, the accuracy of this method is largely dependent on the reliability and extensibility of the shape model, and the model based on normal anatomical structures has very limited effect in the segmentation of irregular structures [ 23 ].

Compared to traditional methods that require manual feature extraction, deep learning can automatically learn the parameters of the model from a large number of data samples, enabling the model to learn complex features and patterns from the data. Recently, deep learning-based methods have gained considerable attention in several image processing applications such as image classification [ 24 ], object detection [ 25 ], image segmentation [ 26 , 27 ], image fusion [ 28 ], image registration [ 29 ] due to their ability to extract features automatically. Methods based on deep learning have become a mainstream in the field of medical image processing. However, there are still several major challenges in multi-organ segmentation tasks. Firstly, there are significant variations in organ sizes, as illustrated by the head and neck in Fig.  1 , the chest in Fig.  2 , the abdomen in Fig.  3 , and the organ size statistics in Fig.  4 . Such size imbalances can lead to poor segmentation performance of the trained network for small organs. Secondly, the inherent noise and low contrast in CT images often result in ambiguous boundaries between different organs or tissue regions, thereby reducing the accuracy of organ boundary segmentation achieved by segmentation networks. Finally, due to safety and ethical concerns, many hospitals do not disclose their datasets, as a result, datasets used to train multiple organ segmentation models are very limited, and many segmentation methods are trained and validated on private datasets, making it difficult to compare with other methods. Consequently, there is an increasing demand for the development of multi-organ segmentation techniques that can accurately segment organs of different sizes, as shown in Fig.  5 .

Schematic diagram of the organs of the head and neck, where the numbers are arranged in order: (1) brainstem, (2) left eye, (3) right eye, (4) left lens, (5) right lens, (6) left optic nerve, (7) right optic nerve, (8) Optical chiasm, (9) left temporal lobe, (10) right temporal lobe, (11) pituitary gland, (12) left parotid gland, (13) right parotid gland, (14) left temporal bone rock, (15) right temporal bone rock, (16) left temporal bone, (17) right temporal bone, (18) left mandibular condyle, (19) right mandibular condyle, (20) spinal cord, (21) left mandible, (22) right mandible. The segmentations and images are from the Automatic Radiotherapy Planning Challenge (StructSeg) in 2019 ( https://structseg2019.grand-challenge.org/Dataset/ )

Schematic diagram of the thoracic organs, where the numbers are arranged in order: (1) left lung, (2) right lung, (3) heart, (4) esophagus, (5) trachea, and (6) spinal cord. The segmentations and images are from the Automatic Radiotherapy Planning Challenge (StructSeg) in 20191

Schematic diagram of the abdominal organs, where the numbers are arranged in order: (1) liver, (2) kidney, (3) spleen, (4) pancreas, (5) aorta, (6) inferior vena cava, (7) stomach, (8) gallbladder, (9) esophagus, (10) right adrenal gland, (11) left adrenal gland, and (12) celiac artery. The segmentations and images are from the Multi-Atlas Labelling Beyond the Cranial Vault (BTCV) by MICCAI [ 34 ]

Illustration of the percentage of voxels in each organ of the head and neck ( a ), abdomen ( b ), and chest ( c ), respectively, which is calculated based on the BTCV data set [ 34 ]

Framework diagram of the overview

Recently, only a few comprehensive reviews have provided detailed summaries of existing multi-organ segmentation methods. For example, Fu et al . [ 30 ] summarized literature of deep learning-based multi-organ segmentation methods up to 2020, providing a comprehensive overview of developments in this field; Vrtovec et al . [ 31 ] systematically analyzed 78 papers published between 2008 and 2020 on the automatic segmentation of OARs in the head and neck. However, these reviews encounter certain issues. Firstly, with the rapid development of technology, many novel methods such as transformer architecture [ 32 ], foundation models [ 33 ] have emerged for addressing multi-organ segmentation, and more public datasets have also been introduced. However, these reviews only encompassed literature up to 2020; secondly, they categorized methods solely based on network design, without categorizing and summarizing specific solutions unique to the challenges of multi-organ segmentation; thirdly, the majority of these reviews primarily covered fully supervised methods and did not provide a summary of papers related to weakly supervised and semi-supervised; lastly, they did not provide a comprehensive summary of the segmentation accuracy for each organ, making it difficult for readers to assess the current segmentation precision for each organ and knew which organs have reached a mature stage of segmentation and which organs still pose challenges.

In this review, we have summarized around the datasets and methods used in multi-organ segmentation. Concerning datasets, we have provided an overview of existing publicly available datasets for multi-organ segmentation and conducted an analysis of these datasets. In terms of methods, we categorized them into fully supervised, weakly supervised, and semi-supervised based on whether complete pixel-level annotations are required. Within the fully supervised methods, we organized the methods according to the network architectures used, input image dimensions, segmentation modules specifically designed for multi-organ segmentation, and the loss functions employed. For weakly supervised and semi-supervised methods, we summarized the latest papers in each subcategory. Detailed information on the datasets and network architectures used in each paper, along with the segmentation accuracy achieved for each organ, has been provided to enable readers to quickly understand the current segmentation accuracy of each organ on the respective datasets. In the discussion section, we have summarized the existing methods in this field and, in conjunction with the latest technologies, discussed future trends in the field of multi-organ segmentation.

The structure of this review is as follows. The first section elaborates on the mathematical definition of multi-organ segmentation and the corresponding evaluation metrics. The second section describes how we conducted literature research and screening based on PRISMA [ 35 ]. The third section presents the literature analysis we retrieved, categorized into two main sections: data and methods. In the data section, we summarize existing public datasets and conduct analysis. In the methods section, we divide into three categories: supervised methods, weakly and semi-supervised methods. In the fourth section, we discuss existing methods and their future prospects, while in the fifth section, we summarize the entire paper.

Definition and evaluation metrics

Let \({\varvec{X}}\) represent the union of input images, \({\varvec{G}}\) represent the union of ground truth labels, \({\varvec{P}}\) represent the union of predicted labels, f represents the neural network, and \({\varvec{\theta}}\) represents its parameters, where \({\varvec{P}}={\varvec{f}}({\varvec{X}};\boldsymbol{ }{\varvec{\theta}})\) .

Given a multi-organ segmentation task, \({\varvec{\Psi}}\) represents the class set of organs to be segmented. \({\left\{{\varvec{x}}\right\}}_{\boldsymbol{*}}\) represents the set of organs annotated in \({\varvec{x}}\) . According to the available annotations, multi-organ segmentation can be implemented according to three learning paradigms, as shown in Fig.  6 : fully supervised learning, weakly supervised learning, and semi-supervised learning. Fully supervised learning means that the labels of all organ are given, which indicates that \(\forall {\varvec{x}}\in {\varvec{X}},\boldsymbol{ }{\left\{{\varvec{x}}\right\}}_{\boldsymbol{*}}={\varvec{\Psi}}\) . Weakly supervised learning often means that the data come from \({\varvec{n}}\) different datasets. However, each dataset provides the annotations of one or more organs but not all organs, which means that \({\varvec{X}}={{\varvec{X}}}_{1}\cup {{\varvec{X}}}_{2}\cup \cdots \cup {{\varvec{X}}}_{n},\boldsymbol{ }\boldsymbol{ }\forall \boldsymbol{ }{{\varvec{x}}}_{k,i}\in {{\varvec{X}}}_{k}, k=\mathrm{1,2},\dots n,\boldsymbol{ }\boldsymbol{ }{\left\{{{\varvec{x}}}_{k,i}\right\}}_{\boldsymbol{*}}\subseteq{\varvec{\Psi}}\) , \(\bigcup_{k=1}^{n}{\left\{{{\varvec{x}}}_{k,i}\right\}}_{\boldsymbol{*}}={\varvec{\Psi}}\) . Here, \({{\varvec{x}}}_{{\varvec{k}},{\varvec{i}}}\) denotes the i th image in \({{\varvec{X}}}_{{\varvec{k}}}\) . Semi-supervised learning indicate that some of the training datasets are fully labeled and others are unlabelled, \({\varvec{X}}={{\varvec{X}}}_{{\varvec{l}}}\cup {{\varvec{X}}}_{{\varvec{u}}}\) . \({{\varvec{X}}}_{{\varvec{l}}}\) represents the fully labeled dataset, \({{\varvec{X}}}_{{\varvec{u}}}\) represents the unlabelled dataset, which indicates that \(\forall {{\varvec{x}}}_{{\varvec{l}}}\in {{\varvec{X}}}_{{\varvec{l}}},\boldsymbol{ }{\left\{{{\varvec{x}}}_{{\varvec{l}}}\right\}}_{\boldsymbol{*}}={\varvec{\Psi}}\) and \(\forall {{\varvec{x}}}_{{\varvec{u}}}\in {{\varvec{X}}}_{{\varvec{u}}},\boldsymbol{ }{\left\{{{\varvec{x}}}_{{\varvec{u}}}\right\}}_{\boldsymbol{*}}={\varvec{\phi}}\) , which represents the empty set , and the size of \({{\varvec{X}}}_{{\varvec{l}}}\) is far less than the one of \({{\varvec{X}}}_{{\varvec{u}}}\) .

General overview of the learning paradigms reviewed in this paper. (The images presented in this figure are sourced from the MICCAI Multi-Atlas Labelling Beyond the Cranial Vault (BTCV) data set [ 34 ].)

The performance of the segmentation methods is typically evaluated using metrics such as the Dice Similarity Coefficient ( DSC ), 95% Hausdorff Distance ( HD95 ) and Mean Surface Distance (MSD) . DSC is a measure of the volume overlap between the predicted outputs and ground truth, HD95 and MSD are measures of the surface distance between them:

where \({P}^{c}\) and \({G}^{c}\) represent the set of predicted pixels and the set of real pixels of the \(c\) class organ, respectively; \({P}_{s}^{c}\) and \({G}_{s}^{c}\) represent the set of predicted pixels and the set of real pixels of the surface of the \(c\) class organ, respectively; and \(d\left({p}_{s}^{c},{G}_{s}^{c}\right)={min}_{{g}_{s}^{c}\in {G}_{s}^{c}}{||{p}_{s}^{c}-{g}_{s}^{c}||}_{2}\) represents the minimal distance from point \({p}_{s}^{c}\) to surface \({G}_{s}^{c}\) . The review reports various methods based on DSC values.

Search protocol

This paper adopts the method proposed by the PRISMA guidelines [ 35 ] to determine the articles included in the analysis. The articles were primarily obtained through Google Scholar. Using the keywords “multi-organ segmentation” and “deep learning”, the search covered the period from January 1, 2016, to December 31, 2023, resulting in a total of 327 articles. We focused on highly cited articles, including those published in top conferences (such as NeurIPS, CVPR, ICCV, ECCV, AAAI, MICCAI, etc.) and top journals (such as TPAMI, TMI, MIA, etc.). Two researchers independently reviewed these articles to determine their eligibility. Among them, 67 articles did not meet the inclusion criteria based on the title and abstract, and 45 complete manuscripts were evaluated separately. In the end, we included 195 studies for analysis.

Public datasets

To obtain high-quality datasets for multi-organ segmentation, numerous research teams have collaborated with medical organizations. A summary of commonly used datasets for validating multi-organ segmentation methods in the head and neck, thorax, and abdomen regions can be found in Table  1 , with references in [ 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. The table also reveals that the amount of annotated data available for deep learning studies remains insufficient.

Datasets analysis

Data play a crucial role in improving model performance. In certain cases, such as lung segmentation, the key issue has shifted from algorithm complexity to dataset quality. Accurate lung segmentation does not necessarily require complex techniques [ 50 ]. Even with simple network architectures, superior results can be achieved with more extensive and heterogeneous private data. The lack of diversity in training data is considered one of the primary obstacles to building robust segmentation models.

Therefore, acquiring large-scale, high-quality, and diverse multi-organ segmentation datasets has become an important direction in current research. Due to the difficulty of annotating medical images, existing publicly available datasets are limited in number and only annotate some organs. Additionally, due to the privacy of medical data, many hospitals cannot openly share their data for training purposes. For the former issue, techniques such as semi-supervised and weakly supervised learning can be utilized to make full use of unlabeled and partially labeled data. Alternatively, human-in-the-loop [ 51 ] techniques can combine human knowledge and experience with machine learning to select samples with the highest annotation value for training. For the latter issue, federated learning [ 52 ] techniques can be applied to achieve joint training of data from various hospitals while protecting data privacy, thus fully utilizing the diversity of the data.

Dataset size

Incorporating unannotated data into training or integration; existing partially labeled data can be fully utilized to enhance model performance, as detailed in Section of Weakly and semi-supervised methods.

Annotation quality

Human-in-the-loop integration of human knowledge and experience minimizes the cost of training accurate predictive models [ 51 ]. By closely collaborating, humans and machines leverage each other’s primary strengths to maximize efficiency. Human-in-the-loop primarily consists of two categories: active learning [ 53 ] and interactive segmentation [ 54 ]. Active learning selects the next batch of annotated samples through algorithms to maximize model performance, presenting an economically effective method for expanding training datasets. Another category, interactive segmentation, expedites the annotation process by allowing expert annotators to interactively correct initial segmentation masks generated by the model.

Wang et al . [ 55 ] comprehensively reviewed core methods of deep active learning, including informative assessment, sampling strategies, integration with other techniques such as semi-supervised and self-supervised learning, and customized active learning works specifically for medical image analysis. Recently, Qu et al . [ 56 ] proposed a novel and systematically effective active learning-based organ segmentation and labeling method. They annotated spleen, liver, kidney, stomach, gallbladder, pancreas, aorta, and inferior vena cava in 8,448 CT volumes. The proposed active learning process generated an attention map, highlighting areas that radiologists need to modify, reducing annotation time from 30.8 years to 3 weeks and accelerating the annotation process by 533 times.

Interactive segmentation in medical imaging typically involves a sequential interactive process, where medical professionals iteratively improve annotation results until the desired level of accuracy is achieved [ 57 ]. In recent years, many deep learning-based interactive segmentation methods have been proposed. Recent advancements in natural image segmentation have witnessed the emergence of segmentation-agnostic models like the Segmentation Anytime Model (SAM) [ 58 , 59 ], demonstrating remarkable versatility and performance in various segmentation tasks. Various large models for medical interactive segmentation have also been proposed, providing powerful tools for generating more high-quality annotated datasets.

Dataset diversity

One significant reason for the limited availability of data for multi-organ segmentation is the issue of data privacy. Many institutions are unable to share their data for training due to privacy concerns. The emergence of federated learning addresses this problem precisely. Federated learning is a distributed learning approach in machine learning aimed at training models across multiple devices or data sources without centralizing the dataset in a single location. In federated learning, model training occurs on local devices, and then locally updated model parameters are sent to a central server, where they are aggregated to update the global model [ 52 ]. This distributed learning approach helps protect user privacy because data do not need to leave devices for model training.

In federated learning, the heterogeneity of statistical data is a crucial research issue. FedAvg is one of the pioneering works to address this issue, using weighted averaging of local weights based on local training scale and has been widely recognized as a baseline for federated learning [ 60 ]. Recently, several federated learning algorithms have been proposed for medical image segmentation tasks. For example, FedSM [ 61 ] employs a model selector to determine the model or data distribution closest to any testing data. Studies [ 62 ] have shown that architectures based on self-attention exhibit stronger robustness to distribution shifts and can converge to better optimal states on heterogeneous data.

Federated learning enables data from multiple sites to participate in training simultaneously without requiring hospitals to disclose their data, thereby enhancing dataset diversity and training more robust segmentation models.

Fully supervised methods

The fully supervised methods require complete annotation of all organs involved in the multi-organ segmentation task. The existing methods can be analyzed from four parts: network architecture, network dimension, image segmentation modules, and network loss function. The network architecture is further divided into single network, cascade network and step-by-step segmentation networks; while network dimension categorizes methods based on the image dimension used (2D, 3D, or multi-view); image segmentation modules refer to modules that are frequently used in multi-organ segmentation to improve segmentation performance, and network loss function summarizes the innovative use of common loss functions for multi-organ segmentation.

Network architecture

Multi-organ segmentation methods can be categorized based on their network architecture, which can be divided into three types: single network, cascade network, and step-by-step segmentation network, which is shown in Fig.  7 . Tables 2 , 3 , 4 summarize the literature related to methods for the segmentation of multi-organ in the head and neck, abdomen and chest based on DSC metrics. Since there are so many organs in the head and neck as well as the abdomen, this paper mainly reports on 9 organs in the head and neck and 7 organs in the abdomen. Tables 5 , 6 summarize the DSC values of other organs.

Three architecture of multi-organ segmentation network

Single network

Cnn-based methods.

CNN can automatically extract features from input image. Multiple neurons are connected to each neuron in next layer, where each layer can perform tasks such as convolution, pooling or loss computation [ 63 ]. CNNs have been successfully applied to medical images, such as brain [ 64 , 65 ] and pancreas [ 66 ] segmentation tasks.

Early CNN-based methods

Earlier CNN-based methods mainly utilized convolutional layers for feature extraction, followed by pooling layers and fully connected layers for final prediction. In the work of Ibragimov and Xing [ 67 ], deep learning techniques were employed for the segmentation of OARs in head and neck CT images for the first time. They trained 13 CNNs for 13 OARs and demonstrated that the CNNs outperformed or were comparable to advanced algorithms in accurately segmenting organs such as the spinal cord, mandible and optic nerve. However, they did not perform well in segmenting organs such as the optical chiasm. Fritscher et al . [ 68 ] incorporated shape location and intensity information with CNN for segmenting the optic nerve, parotid gland, and submandibular gland. Moeskops et al . [ 69 ] investigated whether a single CNN can be used for segmenting multiple tissues across different modalities, including six tissues in brain MR images, pectoral muscles in breast MR images, and coronary arteries in heart CTA images. Their results demonstrated that a single CNN can effectively segment multiple organs across different imaging modalities.

FCN-based methods

Early methods based on CNN showed some improvement in segmentation accuracy compared to traditional methods. However, CNN involves multiple identical computations of overlapping voxels during the convolution operation, which may cause some performance loss. Moreover, the final fully connected network layer in CNN can introduce spatial information loss to the image. To overcome these limitations, Shelhamer et al . [ 70 ] proposed the Fully Convolutional Network (FCN), which utilized transposed convolutional layers to achieve end-to-end segmentation while preserving spatial information. Wang et al . [ 71 ] used FCN with a novel sample selection strategy to segment 16 organs in the abdomen, while Trullo et al . [ 72 ] employed a variant of FCN called SharpMask [ 73 ] to enhance the segmentation performance of 5 organs in the thorax compared to standard FCN.

U-Net-based methods

The U-Net architecture, proposed by Ronneberger et al . [ 74 ], builds upon the FCN framework and consists of an encoder and a decoder, connecting them layer by layer with skip connections that allow for multiscale feature fusion. U-Net has become a widely adopted architecture in multi-organ segmentation [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. For example, Roth et al . [ 79 ] employed U-Net to segment 7 organs in the abdomen with an average Dice value of 0.893. Lambert et al . [ 45 ] proposed a simplified U-Net for segmenting the heart, trachea, aorta, and esophagus of the chest, which improved performance by adding dropout and bilinear interpolation. Apart from U-Net, V-Net [ 90 ] introduced a volumetric, fully convolutional neural network for 3D image segmentation [ 91 , 92 , 93 ]. Gibson et al . [ 91 ] used dense V-Networks to segment 8 organs in the abdomen, while Xu et al . [ 92 ] proposed a probabilistic V-Net model with a conditional variational autoencoder (cVAE) and hierarchical spatial feature transform (HSPT) for abdominal organs segmentation. The nnU-Net [ 94 ] is a novel framework based on U-Net architecture with adaptive pre-processing, data enhancement, and postprocessing techniques, which has demonstrated state-of-the-art performance in various biomedical segmentation challenges [ 95 , 96 , 97 , 98 ]. Podobnik et al . [ 95 ] reported successful results in segmenting 31 OARs in the head and neck using nnU-Net, with both CT and MR images being employed.

GAN-based methods

GAN [ 99 ] usually comprises a pair of competitive networks: generators and discriminators. The generator attempts generate synthetic data that can deceive the discriminator, while the discriminator strives to accurately distinguish between real and generated data. After iterative optimization training, the performance of both networks can be improved. In recent years, several GAN-based multi-organ segmentation methods have been proposed and achieved high segmentation accuracy [ 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 ].

Dong et al . [ 102 ] employed a GAN framework with a set of U-Nets as the generator and a set of FCNs as the discriminator to segment the left lung, right lung, spinal cord, esophagus and heart from chest CT images. The results showed that the adversarial networks enhanced the segmentation performance of most organs, with average DSC values of 0.970, 0.970, 0.900, 0.750, and 0.870 for the above five organs. Tong et al . [ 100 ] proposed a Shape-Constraint GAN (SC-GAN) for automatic segmentation of head and neck OARs from CT and low-field MR images. It used DenseNet [ 108 ], a deep supervised fully convolutional network, to segment organs for prediction and uses a CNN as the discriminator network to correct the prediction errors. The results showed that combining GAN and DenseNet could further improve the segmentation performance of CNN by incorporating original shape constraints.

While GAN can enhance accuracy with its adversarial losses, training a GAN network is challenging and time-consuming since the generator must achieve Nash equilibrium with the discriminator [ 99 ]. Moreover, its adversarial loss, as a shape modifier, can only achieve higher segmentation accuracy when segmenting organs with regular and distinctive shapes (e.g., liver and heart) but may not work well for irregular or tubular structures (such as the pancreas and aorta) [ 109 ].

Transformer-based methods

CNN-based methods have demonstrated impressive effectiveness in segmenting multiple organs across various tasks. However, a significant limitation arises from the inherent shortcomings of the limited perceptual field within the convolutional layers. Specifically, these limitations prevent CNNs from effectively modeling global relationships. This constraint impairs the models' overall performance by limiting their ability to capture and integrate broader contextual information which is critical for accurate segmentation. The self-attention mechanism of transformer [ 32 ] can overcome the long-term dependency problem and achieve superior results compared to CNNs in several tasks, including natural language processing and computer vision. In recent studies, it has been demonstrated that medical image segmentation networks employing transformers can achieve comparable or superior accuracy compared to current state-of-the-art methods [ 110 , 111 , 112 , 113 ].

For instance, Cao et al . [ 114 ] incorporated the transformer into a U-shaped network, named Swin-UNet, to investigate the effectiveness of the pure transformer model in abdominal multi-organ segmentation, which showed promising segmentation accuracy. However, this method requires initializing the network encoder and decoder with the training weights of the Swin transformer on ImageNet. Huang et al . [ 115 ] introduced MISSFormer, a novel architecture for medical image segmentation that addresses convolution's limitations by incorporating an Enhanced Transformer Block. This innovation enables effective capture of long-range dependencies and local context, significantly improving segmentation performance. Furthermore, in contrast to Swin-UNet, this method can achieve comparable segmentation performance without the necessity of pre-training on extensive datasets. Tang et al . [ 116 ] introduce a novel framework for self-supervised pre-training of 3D medical images. This pioneering work includes the first-ever proposal of transformer-based pre-training for 3D medical images, enabling the utilization of the Swin Transformer encoder to enhance fine-tuning for segmentation tasks.

While transformer-based methods can capture long-range dependencies and outperform CNNs in several tasks, they may struggle with the detailed localization of low-resolution features, resulting in coarse segmentation results. This concern is particularly significant in the context of multi-organ segmentation, especially when it involves the segmentation of small-sized organs [ 117 , 118 ].

Hybrid networks

CNNs are proficient at detecting local features but frequently struggle to capture global features effectively. In contrast, transformers can capture long-range feature dependencies but may lose local feature details and result in poor segmentation accuracy for small organs. To overcome the limitations, researchers have explored hybrid methods that combine CNN and transformer frameworks [ 111 , 119 , 120 , 121 , 122 , 123 ].

For example, Suo et al . [ 124 ] proposed the I2-Net, a collaborative learning network that combines features extracted by CNNs and transformers to accurately segment multiple abdominal organs. This method resulted in an enhancement of the segmentation accuracy for small organs by 4.19%, and for medium-sized organs by a range of 1.83% to 3.8%. Kan et al . [ 125 ] proposed ITUnet, which added transformer-extracted features to the output of each block of the CNN-based encoder, obtaining segmentation results that leveraged both local and global information. ITUnet demonstrated better accuracy and robustness than other methods, especially on difficult organs such as the lens. Chen et al . [ 126 ] introduced TransUNet, a network architecture that utilized transformers to build stronger encoders and competitive results for head and neck multi-organ segmentation. Similarly, Hatamizadeh et al. [ 127 ] introduced UNETR and Swin UNETR [ 128 ], which employed transformers (Swin transformer) as encoders and CNNs as decoders. This hybrid method captured both global and local dependencies, leading to improved segmentation accuracy.

In addition to the methods combining CNN and transformer, there are some other hybrid architectures. For example, Chen et al . [ 129 ] integrated U-Net with long short-term memory (LSTM) for chest organ segmentation, and the DSC values of all five organs were above 0.8. Chakravarty et al . [ 130 ] introduced a hybrid architecture that leveraged the strengths of both CNNs and recurrent neural networks (RNNs) to segment the optic disc, nucleus, and left atrium. The hybrid methods effectively merge and harness the advantages of both architectures for accurate segmentation of small and medium-sized organs, which is a crucial research direction for the future.

Cascade network

Segmenting small organs in medical images is challenging because most organs occupy only a small volume in the images, making it difficult for segmentation models to accurately identify them. To address this constraint, researchers have proposed cascade multi-stage methods, which can be categorized into two types. One is coarse-to-fine-based method [ 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ], where the first network is utilized to acquire a coarse segmentation, followed by the second network that refines the coarse outcomes for improved accuracy. The other is localization and segmentation-based method [ 105 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 ], where registration methods or localization networks are used to identify candidate boxes for the location of each organ, which are then input into the segmentation network, which is shown in Fig.  7 (B). Additionally, the first network can provide other information, including organ shape, spatial location, or relative proportions, to enhance the segmentation accuracy of the second network.

Coarse-to-fine-based methods

The coarse-to-fine-based methods first input the original image and its corresponding labels into the first network to obtain probability map. This probability map will multiply the original image and be input into the second network to refine the coarse segmentation, as illustrated in Fig.  7 (A). Over the years, numerous methods utilizing the coarse-to-fine method have been developed for multi-organ segmentation, with references in [ 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ].

Trullo et al . [ 72 ] proposed 2 deep architectures that work synergistically to segment several organs such as the esophagus, heart, aorta, and trachea. In the first stage, probabilistic maps were obtained to learn anatomical constrains. Then, four networks were trained to distinguish each target organ from the background in separate refinements. Zhang et al . [ 133 ] developed a new cascaded network model with Block Level Skip Connections (BLSC) between two networks, allowing the second network to benefit from the features learned by each block in the first network. By leveraging these skip connections, the second network can converge more quickly and effectively. Xie et al . [ 134 ] proposed a new framework named the Recurrent Saliency Transformation Network (RSTN) which used coarse segmentation masks as spatial weights in the fine stage, effectively guiding the network's attention to important regions for accurate segmentation. Moreover, by enabling gradients to be backpropagated from the loss layer to the entire network, the RSTN facilitates joint optimization of the two stages. Ma et al . [ 154 ] presented a comprehensive coarse-to-fine segmentation model for automatic segmentation of multiple OARs in head and neck CT images. This model used a predetermined threshold to classify the initial results of the coarse stage into large and small OARs, and then designed different modules to refine the segmentation results.

This coarse-to-fine method efficiently simplifies the background and enhances the distinctiveness of the target structures. By dividing the segmentation task into two stages, this method achieves better segmentation results for small organs compared to the single-stage method. Nevertheless, it is essential to acknowledge that this method entails certain limitations, including heightened memory usage and extended training times attributed to the necessity of train at least two networks.

Localization and segmentation-based methods

In the localization and segmentation-based method, the first network provides location information and generates a candidate frame, which is then used to extract the Region of Interests (ROIs) from the image. This extracted region, free from interference of other organs or background noise, serves as the input for the second network. By isolating the targeted organ, the segmentation accuracy is improved. The process is illustrated in Fig.  7 (B). The organ location in the first stage can be obtained through registration or localization network, with reference in [ 105 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 ].

Wang et al . [ 142 ], Men et al . [ 143 ], Lei et al . [ 149 ], Francis et al . [ 155 ], and Tang et al . [ 144 ] used neural networks in both stages. In the first stage, networks were used to localize the target OARs by generating bounding boxes. In the second stage, the target OARs were segmented within the bounding boxes. Among them, Wang et al . [ 142 ] and Francis et al . [ 155 ] utilized 3D U-Net in both stages, while Lei et al . [ 149 ] used Faster RCNN to automatically locate the ROI of organs in the first stage. Furthermore, FocusNet [ 105 , 147 ] presented a novel neural network that effectively addresses the challenge of class imbalance in the segmentation of head and neck OARs. The small organs are first localized using the organ localization network, and then high-resolution features of small organs are fed into the segmentation network. Liang et al . [ 146 ] introduced a multi-organ segmentation framework that utilizes multi-view spatial aggregation to integrate the learning of both organ localization and segmentation subnetworks. This framework mitigates the impact of neighboring structures and background regions in the input data, and the proposed fine-grained representation based on ROIs enhances the segmentation accuracy of organs with varying sizes, particularly small organs.

Larsson et al . [ 152 ], Zhao et al . [ 153 ], Ren et al . [ 156 ], and Huang et al . [ 150 ] utilized registration-based methods to localize organs, while CNN was employed for accurate segmentation. Ren et al . [ 156 ] used interleaved cascades of 3D-CNNs to segment each organ, exploiting the high correlation between adjacent tissues. Specifically, the initial segmentation results of a particular tissue can improve the segmentation of its neighboring tissues. Zhao et al . [ 153 ] proposed a flexible knowledge-assisted framework that synergistically integrated deep learning and traditional techniques to improve segmentation accuracy in the second stage.

Localization and segmentation-based methods have proven to enhance the accuracy of organ segmentation by reducing background interference, particularly for small organs. However, this method requires considerable memory and training time, and the accuracy of segmentation is heavily reliant on the accuracy of organ localization. Therefore, improving the localization of organs and enhancing segmentation accuracy are still areas of research that need further exploration in the future.

Other cascade methods

In addition to probability maps and localization information, the first network can also provide other types of information that can be used to improve segmentation accuracy, such as scale information and shape priors. For instance, Tong et al . [ 157 ] combined FCNN with a shape representation model (SRM) for head and neck OARs segmentation. The SRM serves as the first network for learning highly representative shape features in head and neck organs, which are then used to improve the accuracy of the FCNN. The results from comparing the FCNN with and without SRM indicated that the inclusion of SRM greatly raised the segmentation accuracy of 9 organs, which varied in size, morphological complexity, and CT contrasts. Roth et al . [ 158 ] proposed two cascaded FCNs, where low-resolution 3D FCN predictions were upsampled, cropped, and connected to higher-resolution 3D FCN inputs.

Step-by-step segmentation network

In the context of multi-organ segmentation, step-by-step segmentation refers to sequentially segmenting organs in order of increasing complexity, starting with easier-to-segment organs before moving on to more challenging ones, which is shown in Fig.  7 (C). The fundamental assumption is that segmenting more challenging organs (e.g., those with more complex shapes and greater variability) can benefit from the segmentation results of simpler organs processed earlier [ 159 ]. Step-by-step segmentation has been demonstrated to be highly effective for segmenting some of the most challenging organs, such as the pancreas (Hammon et al. [ 160 ]), utilizing surrounding organs (such as the liver and spleen) as supportive structures.

In recent years, many deep learning-based step-by-step segmentation methods have emerged. For example, Zhao et al . [ 161 ] first employed the nnU-Net to segment the kidneys and then to segment kidney tumors based on the segmentation results of the kidneys. Similarly, Christ et al . [ 136 ] first segment the liver, followed by the segmentation of liver tumors based on the segmentation results of the liver. In [ 162 ], organs susceptible to segmentation errors, such as the lungs, are segmented first, followed by the segmentation of less susceptible organs, such as airways, based on lung segmentation. Guo et al . [ 163 ] proposed a method called Stratified Organ at Risk Segmentation (SOARS), which categorizes organs into anchor, intermediate, and small and hard (S&H) categories. Each OAR category uses a different processing framework. Inspired by clinical practice, anchor organs are utilized to guide the segmentation of intermediate and S&H category organs.

Network dimension

Considering the dimension of input images and convolutional kernels, multi-organ segmentation networks can be divided into 2D, 2.5D and 3D architectures, and the differences among three architectures will be discussed in follows.

2D- and 3D-based methods

The 2D multi-organ segmentation network takes as input slices from a three-dimensional medical image, and the convolution kernel is also two-dimensional. Several studies, including those by Men et al . [ 89 ], Trullo et al . [ 72 ], Gibson et al . [ 91 ], Chen et al . [ 164 ], Zhang et al . [ 78 ], and Chen et al . [ 165 ], have utilized 2D networks for multi-organ segmentation. 2D architectures can reduce the GPU memory burden. But CT or MR images are inherently 3D, slicing images into 2D tends to ignore the rich information in the entire image voxel, so 2D models are insufficient for analyzing the complex 3D structures in medical images.

3D multi-organ segmentation networks can extract features directly from 3D medical images by using 3D convolutional kernels. Some studies, such as Roth et al . [ 79 ], Zhu et al . [ 75 ], Gou et al . [ 77 ], and Jain et al . [ 166 ], have employed 3D network for multi-organ segmentation. However, since 3D network requires a large amount of GPU memory, they may face computationally intensive and memory shortage problems. As a result, most 3D network-based methods use sliding windows acting on patches. To overcome the constraints of GPU memory, Zhu et al . [ 75 ] proposed a model called AnatomyNet, which took full-volume of head and neck CT images as inputs and generated masks for all organs to be segmented at once. To balance GPU memory usage and network learning capability, they employed a down-sampling layer solely in the first encoding block, which also preserved information of small anatomical structures.

Multi-view-based methods

Accurate medical image segmentation requires effective use of spatial information among image slices. Inputting 3D images directly to the neural network can lead to high memory usage, while converting 3D images to 2D slices results in the loss of spatial information between slices. As a solution, multi-view-based methods have been proposed, which include using 2.5D neural networks with multiple 2D slices or combining 2D and 3D convolutions. This method can reduce memory usage while maintaining the spatial information between slices, improving the accuracy of medical image segmentation.

The 2.5D-based method uses 2D convolutional kernels and takes in multiple slices as input. The slices can either be a stack of adjacent slices using interslice information [ 167 , 168 ], or slices along three orthogonal directions (axial, coronal, and sagittal) [ 67 , 68 , 148 , 169 ], which is shown in Fig.  8 . Zhou et al . [ 170 ] segmented each 2D slice using FCN by sampling a 3D CT case on three orthogonally oriented slices and then assembled the segmented output (i.e., 2D slice results) back into 3D. Chen et al . [ 165 ] developed a multi-view training method with a majority voting strategy. Wang et al . [ 171 ] used a statistical fusion method to combine segmentation results from three views. Liang et al . [ 148 ] performed context-based iterative refinement training on each of the three views and aggregated all the predicted probability maps to obtain final segmentation results. These methods have shown improved segmentation results compared to the three separate views.

Framework of multi-view-based methods

Tang et al . [ 172 ] proposed a novel method which combines the strengths of 2D and 3D models. This method utilized high-resolution 2D convolution for accurate segmentation and low-resolution 3D convolution for extracting spatial contextual information. A self-attention mechanism controlled the corresponding 3D features to guide 2D segmentation, and experiments demonstrated that this method outperforms both 2D and 3D models. Similarly, Chen et al . [ 164 ] devised a novel convolutional neural network, OrganNet2.5D, that effectively processed diverse planar and depth resolutions by fully utilizing 3D image information. This network combined 2D and 3D convolutions to extract both edge and high-level semantic features.

Some studies only used 2D images to avoid memory and computation problems, but they did not fully exploit the potential of 3D image information. Although 2.5D methods can make better use of multiple views, their ability to extract spatial contextual information is still limited. Current 2.5D methods in multi-organ segmentation aggregate three perspectives at the outcome level, but the intermediate processes are independent of each other, and more effective use of intermediate learning processes is an area for further investigation. Pure 3D networks have a high parameter and computational burden, which limits their depth and performance. As for this reason, some people have begun researching lightweight 3D networks, Zhao et al . [ 173 ] proposed a novel framework based on lightweight network and Knowledge Distillation (KD) for delineating multiple organs from 3D CT volumes. Thus, finding better ways to combine multi-view information to achieve accurate multi-organ segmentation while considering memory and computational resources is a promising research direction.

Image segmentation modules

The design of network architecture is a crucial factor in improving the accuracy of multi-organ segmentation, but the process of designing such a network is quite intricate. In multi-organ segmentation tasks, various special mechanisms, such as dilation convolution module, feature pyramid module, and attention module, have been developed to enhance the accuracy of organ segmentation. These modules increase the perceptual field, combine features of different scales, and concentrate the network on the segmented region, thereby enhancing the accuracy of multi-organ segmentation. Cheng et al . [ 174 ] have explored the efficacy of each module in the network compared with the basic U-Net network for the head and neck segmentation task.

Shape prior module

Shape prior has been shown to be particularly effective for medical images due to the fixed spatial relationships between internal structures. As a result, incorporating anatomical priors in multi-organ segmentation task can significantly enhance the segmentation performance.

There are two main methods used for incorporating anatomical priors in multi-organ segmentation tasks. The first method is based on statistical analysis, which involves calculating the average distribution of organs in a fully labeled dataset. The segmentation network predictions are then guided to be as close as possible to this average distribution of organs [ 66 , 68 , 102 , 175 , 176 ]. The second method involves training a shape representation model that is pretrained using annotations from the training dataset. This model is used as a regularization term to constrain the predictions of the network during training [ 100 , 157 ]. For example, Tappeiner et al . [ 177 ] propose that using stacked convolutional autoencoders as shape priors can enhance segmentation accuracy, both on small datasets and complete datasets. Recently, it has been demonstrated that generative models such as diffusion models [ 178 , 179 ] can learn anatomical priors [ 180 ]. Therefore, utilizing generative models to obtain anatomical prior knowledge is a promising future research direction for improving segmentation performance.

Dilated convolutional module

In conventional CNN, down-sampling and pooling operations are commonly employed to expand the perception field and reduce computation, but these can cause spatial information loss and hinder image reconstruction. Dilated convolution (also referred to as "Atrous") introduces an additional parameter, expansion rate, to the convolution layer, which can allow for the expansion of the perception field without increasing computational cost. Dilated convolution is widely used in multi-organ segmentation tasks [ 66 , 80 , 168 , 181 , 182 ] to enlarge the sampling space and enable the neural network to extract multiscale contextual features across a wider receptive field. For instance, Li et al . [ 183 ] proposed a high-resolution 3D convolutional network architecture that integrates dilated convolutions and residual connections to incorporates large volumetric context. The effectiveness of this approach has been validated in brain segmentation tasks using MR images. Gibson et al . [ 66 ] utilized CNN with dilated convolution to accurately segment organs from abdominal CT images. Men et al . [ 89 ] introduced a novel Deep Dilated Convolutional Neural Network (DDCNN) for rapid and consistent automatic segmentation of clinical target volumes (CTVs) and OARs. Vesal et al . [ 182 ] integrated dilated convolution into the 2D U-Net for segmenting esophagus, heart, aorta, and thoracic trachea.

Multiscale module

Neural networks are composed of layers that progressively extract features from input data. The lower layers capture fine-grained geometric details with a smaller receptive field, providing high-resolution but weaker semantic representation. Conversely, higher layers have a larger receptive field and stronger semantic representation, but lower feature map resolution, which may cause information loss for small targets. To address this, multiscale fusion modules have been proposed, including bottom-up, top-down, and lateral feature pyramids (FPNs) [ 184 ], spatial pooling pyramids (ASPPs) [ 185 ] that combine dilated convolution and multiscale fusion. In multi-organ segmentation tasks, multiscale feature fusion is widely used because of the different sizes of organs. For example, Jia and Wei [ 80 ] introduced the feature pyramid into a multi-organ segmentation network using two opposite feature pyramids (top-down and bottom-up) to handle multiscale changes and improve the segmentation accuracy of small targets. Shi et al . [ 168 ] used the pyramidal structure of lateral connections between encoders and decoders to capture contextual information at multiple scales. Additionally, Srivastava et al . [ 186 ] introduced OARFocalFuseNet, a novel segmentation architecture that utilized a focal modulation scheme for aggregating multiscale contexts in a dedicated resolution stream during multiscale fusion.

Attention module

The attention module is a powerful tool that allows the network to dynamically weight important features. It can leverage the inherent self-attentiveness of the network and is especially useful for multi-organ segmentation tasks [ 101 , 187 ]. There are several kinds of attention mechanisms, such as channel attention, spatial attention, and self-attention, which can be used to selectively emphasize the most informative features.

Squeeze-and-excitation (SE) module [ 188 ] is an effective channel attention technique that enables the network to emphasize important regions in an image. AnatomyNet [ 75 ] utilized 3D SE residual blocks to segment the OARs in the head and neck. This method enabled the extraction of 3D features directly from CT images and dynamically adjusted the mapping of residual features within each channel by generating a channel attention tensor. Liu et al . [ 189 ] proposed a novel network architecture, named Cross-layer Spatial Attention map Fusion CNN (CSAF-CNN), which could integrate the weights of different spatial attentional maps in the network, resulting in significant improvements in segmentation performance. In particular, the average DSC of 22 organs in the head and neck was 72.50%, which outperformed U-Net (63.9%) and SE-UNet (67.9%). Gou et al . [ 77 ] designed a Self-Channel-Spatial-Attention neural network (SCSA-Net) for 3D head and neck OARs segmentation. This network could adaptively enhance both channel and spatial features, and it outperformed SE-Res-Net and SE-Net in segmenting the optic nerve and submandibular gland. Lin et al . [ 190 ] proposed a variance-aware attention U-Net network that embedded variance uncertainty into the attention architecture to improve the attention to error-prone regions (e.g., boundary regions) in multi-organ segmentation. This method significantly improved the segmentation results of small organs and organs with irregular structures (e.g., duodenum, esophagus, gallbladder, and pancreas). Zhang et al . [ 78 ] proposed a novel network called Weaving Attention U-Net (WAU-Net) that combined the U-Net +  + [ 191 ] with axial attention blocks to efficiently model global relationships at different levels of the network. This method achieved competitive performance in segmenting OARs of the head and neck.

Other modules

The dense block [ 108 ] can efficiently use the information of the intermediate layer, and the residual block [ 192 ] can prevent gradient disappearance during backpropagation. These two modules are often embedded in the basic segmentation framework. The convolution kernel of the deformable convolution [ 193 ] can adapt itself to the actual situation and better extract features. Heinrich et al . [ 194 ] proposed the OBELISK-Net, a 3D abdominal multi-organ segmentation architecture that incorporated sparse deformable convolutions with conventional CNNs to enhance segmentation of small organs with large shape variations such as the pancreas and esophagus. The deformable convolutional block proposed by Shen et al . [ 195 ] can handle shape and size variations across organs by generating specific receptive fields with trainable offsets. The strip pooling [ 196 ] module targets long strip structures (e.g., esophagus and spinal cord) by using long pooling instead of square pooling to avoid contamination from unrelated regions and capture remote contextual information. For example, Zhang et al . [ 197 ] utilized a pool of anisotropic strips with three directional receptive fields to capture spatial relationships between multiple organs in the abdomen. Compared to network architectures, network modules have gained widespread use due to their simple design process and ease of integration into various architectures.

Loss function

It is widely recognized that the choice of loss function is of vital importance in determining the segmentation accuracy. In multi-organ segmentation tasks, choosing an appropriate loss function can address the class imbalance issue and improve the segmentation accuracy of small organs. Jadon [ 198 ] has provided a comprehensive overview of commonly used loss functions in semantic segmentation; Ma et al . [ 199 ] systematically summarized common loss functions used in medical image segmentation and evaluated the effectiveness of each loss function across multiple datasets. In the context of multi-organ segmentation, commonly used loss functions include CE loss [ 200 ], Dice loss [ 201 ], Tversky loss [ 202 ], focal loss [ 203 ], and their combinations.

The CE loss (cross-entropy loss) [ 200 ] is a widely used information theoretic measure that compares the predicted output labels with the ground truth. Men et al . [ 89 ], Moeskops et al . [ 95 ], and Zhang et al . [ 78 ] utilized CE loss for multi-organ segmentation. However, in situations where the background pixels greatly outnumber the foreground pixels, CE loss can result in poor segmentation outcomes by heavily biasing the model towards the background. To overcome this issue, the weighted CE loss [ 204 ] added weight parameters to each category based on CE loss, making it better suited for situations with unbalanced sample sizes. Since multi-organ segmentation often faces a significant class imbalance problem, using the weighted CE loss is a more effective strategy than using only the CE loss. As an illustration, Trullo et al . [ 72 ] used a weighted CE loss to segment the heart, esophagus, trachea, and aorta in chest images, while Roth et al . [ 79 ] applied a weighted CE loss for abdomen multi-organ segmentation.

Milletari et al . [ 90 ] proposed the Dice loss to quantify the intersection between volumes, which converted the voxel-based measure to a semantic label overlap measure, becoming a commonly used loss function in segmentation tasks. Ibragimov and Xing [ 67 ] used the Dice loss to segment multiple organs of the head and neck. However, using the Dice loss alone does not completely solve the issue that neural networks tend to perform better on large organs. To address this, Sudre et al . [ 201 ] introduced the weighted Dice score (GDSC), which adapted its Dice values considering the current class size. Shen et al . [ 205 ] assessed the impact of class label frequency on segmentation accuracy by evaluating three types of GDSC (uniform, simple, and square). Gou et al . [ 77 ] employed GDSC for head and neck multi-organ segmentation, while Tappeiner et al . [ 206 ] introduced a class-adaptive Dice loss based on nnU-Net to mitigate high imbalances. The results showcased the method's effectiveness in significantly enhancing segmentation outcomes for class-imbalanced tasks. Kodym et al. [ 207 ] introduced a new loss function named as the batch soft Dice loss function for training the network. Compared to other loss functions and state-of-the-art methods on current datasets, models trained with batch Dice loss achieved optimal performance.

Other losses

The Tversky loss [ 202 ] is an extension of the Dice loss and can be fine-tuned by adjusting its parameters to balance the rates of false positives and false negatives. The focal loss [ 203 ] was originally proposed for object detection to highlight challenging samples during training. Similarly, the focal Tversky loss [ 208 ] assigns less weight to easy-to-segment organs and focuses more on difficult organs. Berzoini et al . [ 81 ] applied the focal Tversky loss to smaller organs, which balances the performance between organs of different sizes and assigns more weight to hard-to-segment small organs, thus solving the class imbalance issue caused by kidneys and bladders. Inspired by the exponential logarithmic loss (ELD-Loss) [ 209 ], Liu et al . [ 189 ] introduced the top-k exponential logarithmic loss (TELD-Loss) to address the issue of class imbalance in head and neck OARs segmentation. Results indicate that the TELD-Loss is a robust method, particularly when dealing with mislabeling problems.

Combined loss

To address the advantages and disadvantages of different loss functions in multi-organ segmentation, researchers have proposed combining multiple loss functions for improved outcomes. The commonly employed method is a weighted sum of Dice loss and CE loss. Dice loss tackles class imbalance, while CE loss enhances curve smoothing. For instance, Isensee et al . [ 94 ] introduced a hybrid loss function that combines Dice loss and CE loss to calculate the similarity between predicted voxels and ground truth. Several other studies, including Isler et al . [ 181 ], Srivastava et al . [ 186 ], Xu et al . [ 92 ], Lin et al . [ 190 ], and Song et al . [ 210 ], have also adopted this weighted combination loss for multi-organ segmentation. Zhu et al . [ 75 ] specifically studied different loss functions for the unbalanced head and neck region and found that combining Dice loss with focal loss was superior to using the ordinary Dice loss alone. Similarly, both Cheng et al . [ 174 ] and Chen et al . [ 164 ] have used this combined loss function in their studies.

Conventional Dice loss may not effectively handle smaller structures, as even a minor misclassification can greatly impact the Dice score. Lei et al . [ 211 ] introduced a novel hardness-aware loss function that prioritizes challenging voxels for improved segmentation accuracy. Song et al . [ 212 ] proposed a dynamic loss weighting algorithm that dynamically assigns larger loss weights to organs that are classified as more difficult to segment based on data and network state, forcing the network to learn more from these organs, thereby maximizing segmentation performance. Designing an appropriate loss function is crucial for optimizing neural networks and significantly enhancing organ segmentation precision. This area of research remains essential and continues to be a critical focus for further advancements.

Weakly supervised methods

Obtaining simultaneous annotations for multiple organs on the same medical image poses a significant challenge in image segmentation. Existing datasets, such as LiTS [ 213 ], KiTS (p19) [ 214 ], and pancreas datasets [ 215 ], typically provide annotations for a single organ. How to utilize these partially annotated datasets to achieve a multi-organ segmentation model has arisen increasing interest.

Early methods involved training a segmentation model for each partially annotated dataset, and then combining the output of each model to obtain multi-organ segmentation results, referred to as multiple networks. Although this method is intuitive, it increases computational complexity and storage space. Later, Chen et al . [ 216 ] improved upon the multiple networks method by introducing a multi-head network. This network consists of a task-shared encoder and multiple task-specific decoders. When an image with annotations for a specific organ is input into the network, only the decoder parameters corresponding to that organ are updated, while the parameters for decoders corresponding to other organs are frozen. Though the multi-head network represents an improvement over multiple networks, this architecture is not flexible and cannot easily adapt to a newly annotated dataset. Recently, various methods have been proposed to use these partially annotated datasets, primarily falling into two categories: conditional network-based methods and pseudo-label-based methods.

Conditional network-based methods

Conditional network-based methods primarily involve embedding conditional information into the segmentation network, thus establishing a relationship between parameters of the segmentation model and the target segmented organs, which is shown in Fig.  9 (a). Considering the way in which conditional information is incorporated into the segmentation network, methods based on conditional networks can be further categorized into task-agnostic and task-specific methods. Task-agnostic methods refer to cases where task information and the feature extraction by the encoder–decoder are independent. Task information is combined with the features extracted by the encoder and subsequently converted into conditional parameters introduced into the final layers of the decoder. Typical methods include DoDNet [ 217 ] and its variations [ 218 ], which utilized dynamic controllers to generate distinct weights for different tasks, and these weights were then incorporated into the final decoder layers to facilitate the segmentation of various organs and tumors.

Framework of partially annotated-based-methods

Task-specific methods involve incorporating task information into the process of segmentation feature extraction by the encoder–decoder. For example, Dmitriev et al. [ 219 ] encoded task-related information into the activation layer between convolutional layers and nonlinear layers of decoder. Tgnet [ 220 ] adopted a task-guided method to design new residual blocks and attention modules for fusing image features with task-specific encoding. CCQ [ 221 ] embedded class relationships among multiple organs or tumors and utilizes learnable query vectors representing semantic concepts of different organs, achieving new state-of-the-art results on large partially annotated MOTS dataset.

However, currently, most methods based on conditional networks encode task information as one-hot labels, neglecting the prior relationships among different organs and tumors. Recently, foundation models [ 33 ] have seen significant development. Contrastive Language-Image Pretraining (CLIP) [ 222 ] can reveal the inherent semantics of anatomical structures by mapping similar concepts closer together in the embedding space. Liu et al . [ 223 ] was among the pioneers in applying CLIP to medical imaging. They introduced a CLIP-driven universal model for abdominal organ segmentation and tumor detection. This model achieved outstanding segmentation results for 25 organs based on 3D CT images and demonstrated advanced performance in detecting six types of abdominal tumors. The model ranked first on the MSD public leaderboard [ 41 ] and achieved state-of-the-art results on BTCV dataset [ 34 ]. However, since CLIP is predominantly trained on natural images, its capacity for generalization on medical images is constrained. Ye et al . [ 224 ] introduced a prompt-driven method that transformed organ category information into learnable vectors. While prompt-based methods could capture the intrinsic relationships between different organs, randomly initialized prompts may not fully encapsulate the information about a specific organ.

Pseudo-label-based methods

Pseudo-label-based methods initially train a segmentation model on each partially annotated dataset. Then, they utilize the trained models to generate pseudo labels for corresponding organs on other datasets, resulting in a fully annotated dataset with pseudo labels. A multi-organ segmentation model is subsequently trained using this dataset, which is shown in Fig.  9 (b). Clearly, the performance of the final multi-organ segmentation model is closely tied to the quality of the generated pseudo-labels. In recent years, numerous methods have been proposed to enhance the quality of these pseudo-labels. Huang et al . [ 225 ] proposed a weight-averaging joint training framework that can correct the noise in the pseudo labels to train a more robust model. Zhang et al . [ 226 ] proposed a multi-teacher knowledge distillation framework, which utilizes pseudo labels predicted by teacher models trained on partially labeled datasets to train a student model for multi-organ segmentation. Lian et al . [ 176 ] improved pseudo-label quality by incorporating anatomical priors for single and multiple organs when training both single-organ and multi-organ segmentation models. For the first time, this method considered the domain gaps between partially annotated datasets and multi-organ annotated datasets. Liu et al . [ 227 ] introduced a novel training framework called COSST, which effectively and efficiently combined comprehensive supervision signals with self-training. To mitigate the impact of pseudo labels, they assessed the reliability of pseudo labels through outlier detection in latent space and excluded the least reliable pseudo labels in each self-training iteration.

Other methods

The issue of partially annotated can also be considered from the perspective of continual learning. Continual learning primarily addresses the problem of non-forgetting, where a model trained in a previous stage can segment several organs. After training, only the well-trained segmentation model is retained, and the segmentation labels and data become invisible. Next state, when new annotated organs become available, the challenge is how to ensure that the current model can both segment the current organs and not forget how to segment the previous organs. Inspired by [ 228 ], Liu et al . [ 229 ] first applied continual learning to aggregate partially annotated datasets in stages, which solved the problem of catastrophic forgetting and the background shift. Xu and Yan [ 230 ] proposed Federated Multi-Encoding U-Net (Fed-MENU), a new method that effectively uses independent datasets with different annotated labels to train a unified model for multi-organ segmentation. The model outperformed any model trained on a single dataset or on all datasets combined. Zhang et al . [ 231 ] proposed an innovative architecture specifically for continuous organ and tumor segmentation, in which a lightweight, class specific head was used to replace the traditional output layer, thereby improving flexibility in adapting to emerging classes. At the same time, CLIP was embedded into the heads of specific organs, which encapsulates the semantic information of each class through extensive image text collaborative training, this information would be an advantage for training new classes with pre-known class names. Ji et al . [ 232 ] introduced a novel CSS framework for the continual segmentation of a total of 143 whole-body organs from four partially labeled datasets. Utilizing a trained and frozen General Encoder alongside continually added and architecturally optimized decoders, this model prevents catastrophic forgetting while accurately segmenting new organs.

Others solved this problem from alternative perspectives. Zhou et al . [ 175 ] proposed a Prior-aware Neural Network (PaNN) that guided the training process based on partially annotated datasets by utilizing prior statistics obtained from a fully labeled dataset. Fang and Yan [ 233 ] and Shi et al . [ 234 ] trained uniform models on partially labeled datasets by designing new networks and proposing specific loss functions.

In multi-organ segmentation tasks, weak annotation not only includes partial annotation, but also includes other forms such as image-level annotation, sparse annotation, and noisy annotation [ 235 ]. For example, Kanavati et al . [ 236 ] proposed a weakly supervised method for the segmentation of liver, spleen, and kidney based on classification forests, where the organs were labeled through scribbles.

Semi-supervised methods

Semi-supervised methods are gaining popularity in organ segmentation due to their ability to enhance segmentation performance while reducing the annotation burden. These methods have found application in diverse medical image segmentation tasks, such as heart segmentation [ 237 , 238 , 239 ], pancreas segmentation [ 240 ], and tumor target region segmentation [ 241 ]. In a comprehensive review by Jiao et al . [ 242 ], the authors categorized semi-supervised learning methods in medical image segmentation into three paradigms: pseudo-label-based, consistency regularization-based, and knowledge prior-based methods. In this work, we specifically focus on exploring semi-supervised methods for multi-organ segmentation.

Ma et al . [ 39 ] proposed a semi-supervised method for abdominal multi-organ segmentation using pseudo-labeling. Initially, a teacher model was trained on labeled datasets to generate pseudo labels for unlabeled datasets. Subsequently, a student model was trained on both the labeled and pseudo-labeled datasets, and the student model replaced the teacher model for final training.

Semi-supervised multi-organ segmentation often employs multi-view methods to leverage information from multiple image planes and improve the reliability of pseudo-labels. Zhou et al . [ 243 ] proposed the DMPCT framework, which incorporated a multi-planar fusion module to iteratively update pseudo-labels for different configurations of unlabeled datasets in abdominal CT images. Xia et al . [ 244 ] proposed the uncertainty-aware multi-view collaborative training (UMCT) method, which employed spatial transformations to create diverse perspectives for training independent deep networks. Subsequently, these networks were collectively trained using multi-view consistency on unlabeled data, resulting in improved segmentation effectiveness.

Apart from collaborative training, consistency-based learning is another effective approach for multi-organ segmentation, given the diverse organ categories and their dense distribution. This method promotes the consistency of network outputs by using different parameters. For example, Lai et al . [ 245 ] proposed a semi-supervised DLUNet, which consisted of two lightweight U-Nets in the training phase. Additionally, for unlabeled data, the outputs from both networks were used to supervise each other, improving the segmentation accuracy of these unlabeled data. This method achieved an average DSC of 0.8718 for 13 organs in the abdomen. Chen et al . [ 246 ] proposed a novel teacher–student semi-supervised multi-organ segmentation model, called MagicNet, which normalized consistency training between teacher and student models by enhancing unlabeled data. MagicNet mainly included two data enhancement strategies, encouraging unlabeled images to learn relative organ semantics (cross-branch) from images and enhancing the segmentation accuracy of small organs (within-branch), Numerous experiments conducted on two common CT multi-organ datasets have demonstrated the effectiveness of MagicNet and were significantly superior to state-of-the-art semi-supervised medical image segmentation methods.

Furthermore, several other methods have been proposed for semi-supervised based method. For example, Lee et al . [ 247 ] developed a method that employed a discriminator module, which incorporated human-in-the-loop quality assurance (QA) to supervise the learning of unlabelled data. The QA scores were used as a loss function for the unlabelled data. Raju et al . [ 248 ] proposed an effective semi-supervised multi-organ segmentation method, CHASe, for liver and lesion segmentation. CHASe leverages co-training and hetero-modality learning within a co-heterogeneous training framework. This framework can be trained on a small single-phase dataset and can be adapted for label-free multi-center and multi-phase clinical data.

This paper systematically summarizes the methods of multi-organ segmentation-based on deep learning, mainly from the aspects of data and methodology. In terms of data, it provides an overview of existing publicly available datasets and conducts an in-depth analysis of data-related issues. In terms of methodology, existing methods are categorized into fully supervised, weakly supervised, and semi-supervised based approaches. The proposal of these methods holds significant research significance in advancing automatic segmentation of multiple organs. Future research trends can be considered from the following aspects:

About datasets

Data play a crucial role in enhancing segmentation performance. Even the simplest models can achieve outstanding performance when trained on a high-quality dataset. However, compared to natural images, there is a shortage of publicly available datasets for multi-organ segmentation, and most methods are trained and tested on private datasets [ 249 ]. As summarized in the supplementary materials, many methods proposed in the literature are trained and validated on their own private datasets. This poses challenges in validating the model's generalization ability. Therefore, it is necessary to create a multi-center public dataset with a large volume of data, extensive coverage, and strong clinical relevance for multi-organ segmentation. In order to fully utilize abundant unlabeled data, combining weakly supervised and semi-supervised techniques, and leveraging human expertise in iterative labeling loops, federated learning techniques can be employed to jointly train models using data from various sites while ensuring privacy.

About fully supervised based methods

Based on the four types of segmentation methods of fully supervised method introduced earlier, future research directions can be considered from the following aspects: firstly, design a new network architecture or investigate how to better integrate different network architectures. Recently, an efficient variant of attention mechanism, Mamba [ 250 , 251 ], has been proposed, surpassing CNN and Transformer in many medical image analysis tasks. Secondly, considering the respective issues of 2D and 3D architectures, designing lightweight 3D networks while maintaining image information and reducing computational burden is a research approach. Additionally, current multi-view methods only aggregate three perspectives at the result level, with the intermediate feature extraction processes being independent of each other. In the future, it can be explored to leverage the intermediate feature extraction processes or incorporate more view information. Thirdly, combining the characteristics of multiple organs, designing novel plug-and-play modules to enhance multi-organ segmentation performance. Finally, due to differences in organ size, shape irregularity, and imaging quality, deep neural networks exhibit inconsistent performance in medical image multi-organ segmentation. Designing loss functions based on the characteristics of different organs to make the network pay more attention to difficult-to-segment organs is an important research direction.

About weakly supervised based methods

At present, many pioneering works have been proposed to address the issue of partially supervised based method, but current works mainly consider that each dataset only annotates one organ and only considers CT images. However, in a more general situation, many publicly available datasets have multiple annotated organs, different datasets may have same organs annotated, and there are also datasets with another modality [ 227 ]. The future trend is how to design a more general architecture to handle cases with overlapping organs and different modalities.

About semi-supervised based methods

In medical science, there is a vast amount of unlabeled datasets, with only a small portion being labeled. However, there is limited discussion on semi-supervised approaches for multi-organ segmentation. However, there are a large number of unlabeled datasets in medicine, with only a small amount of data labeled. Utilizing the latest semi-supervised methods and combining prior information such as organ size and position, to improve the performance of multi-organ segmentation models is an important research direction [ 252 , 253 ].

About considering inter-organ correlation

In multi-organ segmentation, a significant challenge is the imbalance in size and categories among different organs. Therefore, designing a model that can simultaneously segment large organs and fine structures is also challenging. To address this issue, researchers have proposed models specifically tailored for small organs, such as those involving localization before segmentation or the fusion of multiscale features for segmentation. In medical image analysis, segmenting structures with similar sizes or possessing prior spatial relationships can help improve segmentation accuracy. For example, Ren et al . [ 156 ] focused on segmenting small tissues like the optic chiasm and left/right optic nerves. They employed a convolutional neural network (CNN)-based approach with interleaved and cascaded processing to handle various tissues, allowing preliminary segmentation results of one organ to assist in improving the segmentation of other organs and its own segmentation. Qin et al . [ 254 ] considered the correlation between structures when segmenting the trachea, arteries, and veins, including the proximity of arteries to airways and the similarity in strength between airway walls and vessels. Additionally, some researchers [ 255 ] took into account that the spatial relationships between internal structures in medical images are often relatively fixed, such as the spleen always being located at the tail of the pancreas. These prior knowledge can serve as latent variables to transfer knowledge shared across multiple domains, thereby enhancing segmentation accuracy and stability.

About combining foundation model

Traditional methods involve training models for specific tasks on specific datasets. However, the current trend is to fine-tune pretrained foundation models for specific tasks. In recent years, there has been a surge in the development of foundation model, including the Generative Pre-trained Transformer (GPT) model [ 256 ], CLIP [ 222 ], and Segmentation Anything Model (SAM) tailored for segmentation tasks [ 59 ]. These models have achieved breakthrough results on natural images. However, due to their training samples being mostly natural images with only a small portion of medical images, the generalization ability of these models in medical images is limited [ 257 , 258 ]. Recently, there have been many ongoing efforts to fine-tune these models to adapt to medical images [ 58 , 257 ]. For the problem of multi-organ segmentation, it is possible to train a specialized segmentation model for medical images by integrating more medical datasets, or study better fine-tuning methods, as well as integrate knowledge from multiple foundation models to improve the segmentation performance.

We provide a systematic review of 195 studies on multi-organ segmentation-based on deep learning. It covers two main aspects: datasets and methods, encompassing multiple body regions such as the head, neck, chest, and abdomen. We also propose tailored solutions for some of the current challenges and limitations in this field, highlighting future research directions. Our review indicates that deep learning-based multi-organ segmentation algorithms are rapidly advancing towards a new era of more precise, detailed, and automated analysis.

Availability of data and materials

Not applicable.

van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology. 2011;261:719–32.

Article   Google Scholar  

Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. J Med Radiat Sci. 2014;61:131–4.

Pfister DG, Spencer S, Adelstein D, Adkins D, Anzai Y, Brizel DM, et al. Head and neck cancers, version 2.2020, NCCN clinical practice guidelines in oncology. J Natl Comprehensive Cancer Netw. 2020;18:873–98.

Molitoris JK, Diwanji T, Snider JW III, Mossahebi S, Samanta S, Badiyan SN, et al. Advances in the use of motion management and image guidance in radiation therapy treatment for lung cancer. J Thorac Dis. 2018;10:S2437–50.

Vyfhuis MAL, Onyeuku N, Diwanji T, Mossahebi S, Amin NP, Badiyan SN, et al. Advances in proton therapy in lung cancer. Ther Adv Respir Dis. 2018;12:175346661878387.

La Macchia M. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol. 2012.

Round CE, Williams MV, Mee T, Kirkby NF, Cooper T, Hoskin P, Jena R. Radiotherapy demand and activity in England 2006–2020. Clin Oncol. 2013.

Hurkmans CW, Borger JH, Pieters BR, Russell NS, Jansen EPM, Mijnheer BJ. Variability in target volume delineation on CT scans of the breast. Int J Radn Oncol Biol Phys. 2001;50:1366–72.

Rasch C, Steenbakkers R, van Herk M. Target definition in prostate, head, and neck. Seminar Radn Oncol. 2005;15:136–45.

Van de Steene J, Linthout N, de Mey J, Vinh-Hung V, Claassens C, Noppen M, et al. Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol. 2002;62:37–49.

Breunig J, Hernandez S, Lin J, Alsager S, Dumstorf C, Price J, et al. A system for continual quality improvement of normal tissue delineation for radiation therapy treatment planning. Int J Radn Oncol Biol Phys. 2012;83:703–8.

Chen X, Pan L. A survey of graph cuts/graph search based medical image segmentation. IEEE Rev Biomed Eng. 2018;11:112–24.

El Naqa I, Yang D, Apte A, Khullar D, Mutic S, Zheng J, et al. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planninga): concurrent multimodality image segmentation for radiotherapy treatment planning. Med Phys. 2007;34:4738–49.

Pratondo A, Chui C-K, Ong S-H. Robust edge-stop functions for edge-based active contour models in medical image segmentation. IEEE Signal Process Lett. 2016;23:222–6.

Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, et al. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging. 2003;22:137–54.

Saranathan AM, Parente M. Threshold based segmentation method for hyperspectral images. 2013 5th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). Gainesville, FL, USA: IEEE; 2013. p. 1–4. http://ieeexplore.ieee.org/document/8080656/ . Accessed 8 Oct 2022.

Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Machine Intell. 2000;22:888–905.

Thool RC, Vyavahare AJ. Segmentation using region growing algorithm based on CLAHE for medical images. Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom2012). Bangalore, India: Institution of Engineering and Technology; 2012. p. 182–5. https://doi.org/10.1049/cp.2012.2522

Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, van Ginneken B. Multi-Atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging. 2009;28:1000–10.

Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage. 2009;46:726–38.

Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, et al. Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging. 2008;27:1189–201.

Qazi AA, Pekar V, Kim J, Xie J, Breen SL, Jaffray DA. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach: feature-driven model-based segmentation. Med Phys. 2011;38:6160–70.

Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009.

Smirnov EA, Timoshenko DM, Andrianov SN. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks. AASRI Procedia. 2014;6:89–94.

Mobiny A, Van Nguyen H. Fast CapsNet for Lung Cancer Screening. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention—MICCAI 2018. Cham: Springer International Publishing; 2018. p. 741–9. https://doi.org/10.1007/978-3-030-00934-2_82

Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK. Recurrent residual U-Net for medical image segmentation. J Med Imag. 2019;6:1.

Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi AK. Medical image segmentation using deep learning: a survey. IET Image Proc. 2022;16:1243–67.

Huang B, Yang F, Yin M, Mo X, Zhong C. A review of multimodal medical image fusion techniques. Comput Math Methods Med. 2020;2020:8279342.

Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. Deep learning in medical image registration: a review. Phys Med Biol. 2020;65:20TR01.

Lei Y, Fu Y, Wang T, Qiu RLJ, Curran WJ, Liu T, et al. Deep Learning in multi-organ segmentation. 2020; https://arxiv.org/abs/2001.10619 . Accessed 30 Sep 2022.

Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B. Auto-segmentation of organs at risk for head and neck radiotherapy planning: from atlas-based to deep learning methods. Med Phys. 2020.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. arXiv:170603762 [cs]. 2017; http://arxiv.org/abs/1706.03762 .

Rishi Bommasani, Drew A. Hudson,et al. On the opportunities and risks of foundation models. https://doi.org/10.48550/arXiv.2108.07258 .

(Author Name Not Available). Segmentation Outside the Cranial Vault Challenge. 2015; https://repo-prod.prod.sagebase.org/repo/v1/doi/locate?id=syn3193805&type=ENTITY . Accessed 3 Oct 2022.

Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021.

Raudaschl PF, Zaffino P, Sharp GC, Spadea MF, Chen A, Dawant BM, et al. Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015. Med Phys. 2017;44:2020–36.

Yang J, Veeraraghavan H, Armato SG, Farahani K, Kirby JS, Kalpathy-Kramer J, et al. Autosegmentation for thoracic radiation treatment planning: a grand challenge at AAPM 2017. Med Phys. 2018;45:4568–81.

Kavur AE, Gezer NS, Barış M, Aslan S, Conze P-H, Groza V, et al. CHAOS challenge—combined (CT-MR) healthy abdominal organ segmentation. Med Image Anal. 2021;69:101950.

Ma J, Zhang Y, Gu S, Zhu C, Ge C, Zhang Y, et al. AbdomenCT-1K: is abdominal organ segmentation a solved problem. IEEE Trans Pattern Anal Mach Intell. 2021;44:1–1.

Google Scholar  

Babier A, Zhang B, Mahmood R, Moore KL, Purdie TG, McNiven AL, Chan TC. OpenKBP: The open-access knowledge-based planning grand challenge. Medical Physics. 2021; https://doi.org/10.48550/arXiv.2011.14076 .

Antonelli M, Reinke A, Bakas S, et al. The medical segmentation Decathlon. https://doi.org/10.48550/arXiv.2106.05735 .

Ji Y, Bai H, Ge C, Yang J, Zhu Y, Zhang R, Li Z, Zhanng L, Ma W, Wan X, Luo P. AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. 2022; https://doi.org/10.48550/arXiv.2206.08023 .

Luo X, Liao W, Xiao J, Chen J, Song T, Zhang X, Li K, Metaxas DN, Wang G, Zhang S. WORD: revisiting organs segmentation in the whole abdominal region. 2023; https://doi.org/10.48550/arXiv.2111.02403 .

Rister B, Yi D, Shivakumar K, Nobashi T, Rubin DL. CT-ORG, a new dataset for multiple organ segmentation in computed tomography. 2020; https://doi.org/10.1038/s41597-020-00715-8

Lambert Z, Petitjean C, Dubray B, Ruan S. SegTHOR: segmentation of thoracic organs at risk in CT images. 2019; https://arxiv.org/abs/1912.05950 . Accessed 3 Oct 2022.

Podobnik G, Strojan P, Peterlin P, Ibragimov B, Vrtovec T. HaN-Seg: the head and neck organ-at-risk CT and MR segmentation dataset. Med Phys. 2023;50:1917–27.

Luo X et al. SegRap2023: a benchmark of organs-at-risk and gross tumor volume segmentation for radiotherapy planning of nasopharyngeal carcinoma. arXiv: 231209576. 2023;

Ma J, Wang B. Fast, low-resource, and accurate organ and pan-cancer segmentation in abdomen ct. international conference on medical image computing and computer assisted intervention (MICCAI) 2023 (MICCAI 2023). 2023.

Wasserthal J et al. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. Radiol Artif Intell. 2023.

Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp. 2020.

Xingjiao Wu, Xiao L, Sun Y, Zhang J, Ma T, He L. A survey of human-in-the-loop for machine learning. Futur Gener Comput Syst. 2022;135:364–81.

Rauniyar A, Hagos DH, Jha D, Håkegård JE, Bagci U, Rawat DB, Vlassov V. Federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions. IEEE Int Things J. 2023. https://doi.org/10.1109/JIOT.2023.3329061 .

Budd S, Robinson EC, Kainz B. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med Image Anal. 2021. https://doi.org/10.1016/j.media.2021.102062 .

Zhang Y, Chen J, Ma X, Wang G, Bhatti UA, Huang M. Interactive medical image annotation using improved attention U-net with compound geodesic distance. Expert systems with applications. 2024.

Wang H, Jin Q, Li S, Liu S, Wang M, Song Z. A comprehensive survey on deep active learning and its applications in medical image analysis. arXiv:231014230. 2023;

AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks. Conference on Neural Information Processing Systems (NeurIPS 2023). https://doi.org/10.48550/arXiv.2305.09666

Zhou T, Li L, Bredell G, Li J, Unkelbach J, Konukoglu E. Volumetric memory network for interactive medical image segmentation. Medical Image Analysis. 2023.

Wu J, Fu R, Fang H, Liu Y, Wang Z, Xu Y, Jin Y, Arbel T. The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Thanks to its. Medical SAM adapter: adapting segment anything model for medical image segmentation. 2023; https://doi.org/10.48550/arXiv.2304.12620 .

Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment Anything. 2023; https://arxiv.org/abs/2304.02643 . Accessed 21 May 2023.

McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA. Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th international conference on artificial intelligence and statistics, PMLR. 2017; 54:1273–82.

Xu A, Li W, Guo P, Yang D, Roth HR, Hatamizadeh A, Zhao C, Xu D, Huang H, Xu Z. Closing the generalization gap of cross-silo federated medical image segmentation. CVPR. 2022.

Qu L, Zhou Y, Liang PP, Xia Y, Wang F, Adeli E, Fei-Fei L, Rubin D. Rethinking architecture design for tackling data heterogeneity in federated learning. CVPR. 2022.

LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation applied to handwritten Zip code recognition. Neural Comput. 1989;1:541–51.

Karthik R, Menaka R, Johnson A, Anand S. Neuroimaging and deep learning for brain stroke detection—a review of recent advancements and future prospects. Comput Methods Programs Biomed. 2020;197:105728.

Zhao X, Chen K, Wu G, Zhang G, Zhou X, Lv C, et al. Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema. Eur Radiol. 2021;31:5012–20.

Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S, editors., et al., Medical image computing and computer assisted intervention—MICCAI 2017. Cham: Springer International Publishing; 2017. p. 728–36. https://doi.org/10.1007/978-3-319-66182-7_83 .

Chapter   Google Scholar  

Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44:547–57.

Fritscher K, Raudaschl P, Zaffino P, Spadea MF, Sharp GC, Schubert R. Deep neural networks for fast segmentation of 3D medical images. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors. Medical image computing and computer-assisted intervention—MICCAI 2016. Cham: Springer International Publishing; 2016. p. 158–65. https://doi.org/10.1007/978-3-319-46723-8_19 .

Moeskops P, Wolterink JM, van der Velden BHM, Gilhuijs KGA, Leiner T, Viergever MA, et al. Deep learning for multi-task medical image segmentation in multiple modalities. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors., et al., Medical image computing and computer-assisted intervention—MICCAI 2016. Cham: Springer International Publishing; 2016. p. 478–86. https://doi.org/10.1007/978-3-319-46723-8_55 .

Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640–51.

Wang Y, Zhou Y, Tang P, Shen W, Fishman EK, Yuille AL. Training multi-organ segmentation networks with sample selection by relaxed upper confident bound. 2018; https://arxiv.org/abs/1804.02595 . Accessed 14 Nov 2022.

Trullo R, Petitjean C, Ruan S, Dubray B, Nie D, Shen D. Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and conditional random fields. 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). Melbourne, Australia: IEEE; 2017. p. 1003–6. http://ieeexplore.ieee.org/document/7950685/ . Accessed 3 Oct 2022.

Pinheiro PO, Lin T-Y, Collobert R, Dollàr P. Learning to refine object segments. 2016; https://arxiv.org/abs/1603.08695 . Accessed 20 Nov 2022.

Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention—MICCAI 2015. Cham: Springer International Publishing; 2015. p. 234–41. https://doi.org/10.1007/978-3-319-24574-4_28 .

Zhu W, Huang Y, Zeng L, Chen X, Liu Y, Qian Z, et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys. 2019;46:576–89.

van Rooij W, Dahele M, Ribeiro Brandao H, Delaney AR, Slotman BJ, Verbakel WF. Deep learning-based delineation of head and neck organs at risk: geometric and dosimetric evaluation. Int J Radiation Oncol Biol Phys. 2019;104:677–84.

Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images. Phys Med Biol. 2020;65:245034.

Zhang Z, Zhao T, Gay H, Zhang W, Sun B. Weaving attention U-net: a novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images. Med Phys. 2021;48:7052–62.

Roth HR, Shen C, Oda H, Oda M, Hayashi Y, Misawa K, et al. Deep learning and its application to medical image segmentation. 2018; https://doi.org/10.11409/mit.36.63

Jia C, Wei J. AMO-Net: abdominal multi-organ segmentation in MRI with a extend Unet. 2021 IEEE 4th advanced information management, communicates, electronic and automation control conference (IMCEC) [Internet]. Chongqing, China: IEEE; 2021. p. 1770–5. https://ieeexplore.ieee.org/document/9482216/ . Accessed 14 Nov 2022.

Berzoini R, Colombo AA, Bardini S, Conelli A, D’Arnese E, Santambrogio MD. An optimized U-Net for unbalanced multi-organ segmentation. 2022 44th annual international conference of the IEEE engineering in medicine and biology society (EMBC). Glasgow, Scotland, United Kingdom: IEEE; 2022. p. 3764–7. https://ieeexplore.ieee.org/document/9871288/ . Accessed 8 Nov 2022.

Liu Y, Lei Y, Fu Y, Wang T, Zhou J, Jiang X, et al. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI. Med Phys. 2020;47:4294–302.

Cros S, Vorontsov E, Kadoury S. Managing class imbalance in multi-organ CT segmentation in head and neck cancer patients. 2021 IEEE 18th international symposium on biomedical imaging (ISBI). Nice, France: IEEE; 2021. p. 1360–4. https://ieeexplore.ieee.org/document/9433991/ . Accessed 14 Nov 2022.

Jiang J, Elguindi S, Berry SL, Onochie I, Cervino L, Deasy JO, et al. Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT. Med Phys. 2022;49:5244–57.

Francis S, Pooloth G, Singam SBS, Puzhakkal N, Pulinthanathu Narayanan P, Pottekkattuvalappil BJ. SABOS-Net : self-supervised attention based network for automatic organ segmentation of head and neck CT images. Int J Imaging Syst Tech. 2022. https://doi.org/10.1002/ima.22802 .

Vu CC, Siddiqui ZA, Zamdborg L, Thompson AB, Quinn TJ, Castillo E, et al. Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology—use of non-domain transfer learning. J Appl Clin Med Phys. 2020;21:108–13.

Mahmood H, Shamsul Islam SM, Hill J, Tay G. Rapid segmentation of thoracic organs using U-net architecture. 2021 digital image computing: techniques and applications (DICTA). Gold Coast, Australia: IEEE; 2021. p. 01–6. https://ieeexplore.ieee.org/document/9647312/ . Accessed 14 Nov 2022.

Hong J, Zhang Y-D, Chen W. Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation. Knowl-Based Syst. 2022;250:109155.

Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44:6377–89.

Milletari F, Navab N, Ahmadi S-A. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016; https://arxiv.org/abs/1606.04797 . Accessed 20 Nov 2022.

Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal CT With dense V-networks. IEEE Trans Med Imaging. 2018;37:1822–34.

Xu M, Guo H, Zhang J, Yan K, Lu L. A New Probabilistic V-Net model with hierarchical spatial feature transform for efficient abdominal multi-organ segmentation. 2022; https://arxiv.org/abs/2208.01382 . Accessed 3 Oct 2022.

Fu S, Lu Y, Wang Y, Zhou Y, Shen W, Fishman E, et al. Domain adaptive relational reasoning for 3D multi-organ segmentation. 2020; https://arxiv.org/abs/2005.09120 . Accessed 8 Oct 2022.

Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.

Podobnik G, Ibragimov B, Strojan P, Peterlin P, Vrtovec T. Segmentation of organs-at-risk from Ct and Mr images of the head and neck: baseline results. 2022 IEEE 19th international symposium on biomedical imaging (ISBI). Kolkata, India: IEEE; 2022. p. 1–4. https://ieeexplore.ieee.org/document/9761433/ . Accessed 14 Nov 2022.

Isensee F, Jäger PF, Full PM, Vollmuth P, Maier-Hein KH. nnU-Net for Brain Tumor Segmentation. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer International Publishing; 2021.

Zhang G, Yang Z, Huo B, Chai S, Jiang S. Multiorgan segmentation from partially labeled datasets with conditional nnU-Net. Comput Biol Med. 2021;136:104658.

Altini N, Brunetti A, Napoletano VP, Girardi F, Allegretti E, Hussain SM, et al. A fusion biopsy framework for prostate cancer based on deformable superellipses and nnU-Net. Bioengineering. 2022;9:343.

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. arXiv; 2014. http://arxiv.org/abs/1406.2661 . Accessed 3 Oct 2022.

Tong N, Gou S, Yang S, Cao M, Sheng K. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images. Med Phys. 2019;46:2669–82.

Cai J, Xia Y, Yang D, Xu D, Yang L, Roth H. End-to-end adversarial shape learning for abdomen organ deep segmentation. In: Suk H-I, Liu M, Yan P, Lian C, editors. Machine learning in medical imaging. Cham: Springer International Publishing; 2019. p. 124–32. https://doi.org/10.1007/978-3-030-32692-0_15 .

Dong X, Lei Y, Wang T, Thomas M, Tang L, Curran WJ, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys. 2019;46:2157–68.

Trullo R, Petitjean C, Dubray B, Ruan S. Multiorgan segmentation using distance-aware adversarial networks. J Med Imag. 2019;6:1.

Mahmood F, Borders D, Chen RJ, Mckay GN, Salimian KJ, Baras A, et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans Med Imaging. 2020;39:3257–67.

Gao Y, Huang R, Yang Y, Zhang J, Shao K, Tao C, et al. FocusNetv2: imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images. Med Image Anal. 2021;67:101831.

Fang H, Fang Y, Yang X. Multi-organ segmentation network with adversarial performance validator. 2022; https://arxiv.org/abs/2204.07850 . Accessed 17 Nov 2022.

Kumar V, Sharma MK, Jehadeesan R, Venkatraman B, Sheet D. Adversarial training of deep convolutional neural network for multi-organ segmentation from multi-sequence MRI of the abdomen. 2021 international conference on intelligent technologies (CONIT). Hubli, India: IEEE; 2021. p. 1–6. https://ieeexplore.ieee.org/document/9498339/ . Accessed 14 Nov 2022.

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on computer vision and pattern recognition (CVPR). Honolulu, HI: IEEE; 2017. p. 2261–9. https://ieeexplore.ieee.org/document/8099726/ . Accessed 14 Nov 2022.

Jeong JJ, Tariq A, Adejumo T, Trivedi H, Gichoya JW, Banerjee I. Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. J Digit Imaging. 2022;35:137–52.

Gao Y, Zhou M, Metaxas D. UTNet: a hybrid transformer architecture for medical image segmentation. 2021; https://arxiv.org/abs/2107.00781 . Accessed 3 Oct 2022.

Chang Y, Menghan H, Guangtao Z, Xiao-Ping Z. TransClaw U-Net: Claw U-Net with transformers for medical image segmentation. 2021; https://arxiv.org/abs/2107.05188 . Accessed 3 Oct 2022.

Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM. Medical transformer: gated axial-attention for medical image segmentation. arXiv; 2021. http://arxiv.org/abs/2102.10662 . Accessed 3 Oct 2022.

Pan S, Lei Y, Wang T, Wynne J, Chang C-W, Roper J, et al. Male pelvic multi-organ segmentation using token-based transformer Vnet. Phys Med Biol. 2022;67:205012.

Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv; 2021. http://arxiv.org/abs/2105.05537 . Accessed 3 Oct 2022.

Huang X, Deng Z, Li D, Yuan X. MISSFormer: an effective medical image segmentation transformer. arXiv; 2021. http://arxiv.org/abs/2109.07162 . Accessed 3 Oct 2022.

Tang Y, Yang D, Li W, Roth HR, Landman B, Xu D, Nath V, Hatamizadeh A.. Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis. CVPR. 2022;

Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH, Zhang L. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. CVPR. 2021;

Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Fu H. Transformers in medical imaging: a survey. Med Image Anal. 2023;88:102802.

Xie Y, Zhang J, Shen C, Xia Y. CoTr: Efficiently bridging CNN and transformer for 3D medical image segmentation. arXiv; 2021. http://arxiv.org/abs/2103.03024 . Accessed 3 Oct 2022.

Wang H, Cao P, Wang J, Zaiane OR. UCTransNet: Rethinking the skip connections in U-Net from a channel-wise perspective with transformer. arXiv; 2022. http://arxiv.org/abs/2109.04335 . Accessed 3 Oct 2022.

Wang H, Xie S, Lin L, Iwamoto Y, Han X-H, Chen Y-W, et al. Mixed transformer U-Net For medical image segmentation. 2021; https://arxiv.org/abs/2111.04734 Accessed 3 Oct 2022.

Xu G, Wu X, Zhang X, He X. LeViT-UNet: Make faster encoders with transformer for medical image segmentation. arXiv; 2021. http://arxiv.org/abs/2107.08623 Accessed 3 Oct 2022.

Zhang Y, Liu H, Hu Q. TransFuse: fusing transformers and CNNs for Medical image segmentation. 2021; https://arxiv.org/abs/2102.08005 Accessed 23 Oct 2022.

Suo C, Li X, Tan D, Zhang Y, Gao X. I2-Net: intra- and inter-scale collaborative learning network for abdominal multi-organ segmentation. proceedings of the 2022 international conference on multimedia retrieval. Newark NJ USA: ACM; 2022. p. 654–60. https://doi.org/10.1145/3512527.3531420. Accessed 3 Oct 2022.

Kan H, Shi J, Zhao M, Wang Z, Han W, An H, et al. ITUnet: integration of transformers and unet for organs-at-risk segmentation. 2022 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC). Glasgow, Scotland, United Kingdom: IEEE; 2022. p. 2123–7. https://ieeexplore.ieee.org/document/9871945/ Accessed 3 Oct 2022.

Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et al. TransUNet: transformers make strong encoders for medical image segmentation. 2021; https://arxiv.org/abs/2102.04306 Accessed 3 Oct 2022.

Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, et al. UNETR: Transformers for 3D medical image segmentation. 2022 IEEE/CVF winter conference on applications of computer vision (WACV). Waikoloa, HI, USA: IEEE; 2022. p. 1748–58. https://ieeexplore.ieee.org/document/9706678/ Accessed 3 Oct 2022.

Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. arXiv:220101266. 2022.

Chen P-H, Huang C-H, Hung S-K, Chen L-C, Hsieh H-L, Chiou W-Y, et al. Attention-LSTM Fused U-Net architecture for organ segmentation in CT images. 2020 international symposium on computer, consumer and control (IS3C). Taichung city, Taiwan: IEEE; 2020. p. 304–7. https://ieeexplore.ieee.org/document/9394166/ Accessed 20 Nov 2022.

Chakravarty A, Sivaswamy J. RACE-Net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform. 2019;23:1151–62.

Tappeiner E, Pröll S, Hönig M, Raudaschl PF, Zaffino P, Spadea MF, et al. Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. Int J CARS. 2019;14:745–54.

Hu P, Wu F, Peng J, Bao Y, Chen F, Kong D. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int J CARS. 2017;12:399–411.

Zhang L, Zhang J, Shen P, Zhu G, Li P, Lu X, et al. Block level skip connections across cascaded V-Net for multi-organ segmentation. IEEE Trans Med Imaging. 2020;39:2782–93.

Xie L, Yu Q, Zhou Y, Wang Y, Fishman EK, Yuille AL. Recurrent saliency transformation network for tiny target segmentation in abdominal CT scans. IEEE Trans Med Imaging. 2020;39:514–25.

Lee HH, Tang Y, Bao S, Abramson RG, Huo Y, Landman BA. Rap-Net: Coarse-To-Fine multi-organ segmentation with single random anatomical prior. 2021 IEEE 18th international symposium on biomedical imaging (ISBI). Nice, France: IEEE; 2021. p. 1491–4. https://ieeexplore.ieee.org/document/9433975/ . Accessed 3 Oct 2022.

Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. 2016. p. 415–23. http://arxiv.org/abs/1610.02177 . Accessed 3 Oct 2022.

Lachinov D, Vasiliev E, Turlapov V. Glioma Segmentation with Cascaded UNet. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, editors. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing; 2019. p. 189–98. https://doi.org/10.1007/978-3-030-11726-9_17

Li S, Chen Y, Yang S, Luo W. Cascade Dense-Unet for Prostate Segmentation in MR Images. In: Huang D-S, Bevilacqua V, Premaratne P, editors. Intelligent Computing Theories and Application. Cham: Springer International Publishing; 2019. p. 481–90. https://doi.org/10.1007/978-3-030-26763-6_46 .

Kakeya H, Okada T, Oshiro Y. 3D U-JAPA-Net: Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018 [cited 2022 Oct 8]. p. 426–33. https://doi.org/10.1007/978-3-030-00937-3_49

Trullo R, Petitjean C, Nie D, Shen D, Ruan S. Joint segmentation of multiple thoracic organs in CT images with two collaborative deep architectures. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support. 2017;2017(10553):21–9.

Cao Z, Yu B, Lei B, Ying H, Zhang X, Chen DZ, et al. Cascaded SE-ResUnet for segmentation of thoracic organs at risk. Neurocomputing. 2021;453:357–68.

Wang Y, Zhao L, Wang M, Song Z. Organ at risk segmentation in head and neck CT images using a two-stage segmentation framework based on 3D U-Net. IEEE Access. 2019;7:144591–602.

Men K, Geng H, Cheng C, Zhong H, Huang M, Fan Y, et al. Technical note: more accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades. Med Phys. 2018. https://doi.org/10.1002/mp.13296 .

Tang H, Chen X, Liu Y, Lu Z, You J, Yang M, et al. Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat Mach Intell. 2019;1:480–91.

Yang Q, Zhang S, Sun X, Sun J, Yuan K. Automatic segmentation of head-neck organs by Multi-MODE CNNs for radiation therapy. 2019 international conference on medical imaging physics and engineering (ICMIPE). Shenzhen, China: IEEE; 2019. p. 1–5. https://ieeexplore.ieee.org/document/9098166/ . Accessed 8 Oct 2022.

Liang S, Tang F, Huang X, Yang K, Zhong T, Hu R, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. Eur Radiol. 2019;29:1961–7.

Gao Y, Huang R, Chen M, Wang Z, Deng J, Chen Y, et al. FocusNet: imbalanced large and small organ segmentation with an end-to-end deep neural network for head and neck CT images. arXiv; 2019. http://arxiv.org/abs/1907.12056 . Accessed 3 Oct 2022.

Liang S, Thung K-H, Nie D, Zhang Y, Shen D. Multi-view spatial aggregation framework for joint localization and segmentation of organs at risk in head and neck CT images. IEEE Trans Med Imaging. 2020;39:2794–805.

Lei Y, Zhou J, Dong X, Wang T, Mao H, McDonald M, et al. Multi-organ segmentation in head and neck MRI using U-Faster-RCNN. In: Landman BA, Išgum I, editors. Medical Imaging 2020: Image Processing. Houston, United States: SPIE; 2020 [cited 2022 Oct 3]. p. 117. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/2549596/Multi-organ-segmentation-in-head-and-neck-MRI-using-U/ https://doi.org/10.1117/12.2549596.full

Huang B, Ye Y, Xu Z, Cai Z, He Y, Zhong Z, et al. 3D Lightweight Network for Simultaneous Registration and Segmentation of Organs-at-Risk in CT Images of Head and Neck Cancer. IEEE Trans Med Imaging. 2022;41:951–64.

Korte JC, Hardcastle N, Ng SP, Clark B, Kron T, Jackson P. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging. Med Phys. 2021;48:7757–72.

Larsson M, Zhang Y, Kahl F. Robust abdominal organ segmentation using regional convolutional neural networks. Appl Soft Comput. 2018;70:465–71.

Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, et al. Knowledge-aided convolutional neural network for small organ segmentation. IEEE J Biomed Health Inform. 2019;23:1363–73.

Ma Q, Zu C, Wu X, Zhou J, Wang Y, et al. Coarse-to-fine segmentation of organs at risk in nasopharyngeal carcinoma radiotherapy. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, et al., editors. Medical image computing and computer assisted intervention—MICCAI 2021. Cham: Springer International Publishing; 2021. p. 358–68. https://doi.org/10.1007/978-3-030-87193-2_34 .

Francis S, Jayaraj PB, Pournami PN, Thomas M, Jose AT, Binu AJ, et al. ThoraxNet: a 3D U-Net based two-stage framework for OAR segmentation on thoracic CT images. Phys Eng Sci Med. 2022;45:189–203.

Ren X, Xiang L, Nie D, Shao Y, Zhang H, Shen D, et al. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images. Med Phys. 2018;45:2063–75.

Tong N, Gou S, Yang S, Ruan D, Sheng K. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med Phys. 2018;45:4558–67.

Roth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, et al. Hierarchical 3D fully convolutional networks for multi-organ segmentation. arXiv; 2017. http://arxiv.org/abs/1704.06382 . Accessed 3 Oct 2022.

Kéchichian, R., Valette, S., Sdika, M., Desvignes, M. Automatic 3D Multiorgan Segmentation via Clustering and Graph Cut Using Spatial Relations and Hierarchically-Registered Atlases. Medical Computer Vision: Algorithms for Big Data MCV. Lecture Notes in Computer Science. 2014; https://doi.org/10.1007/978-3-319-13972-2_19

Hammon M, Cavallaro A, Erdt M, Dankerl P, Kirschner M, Drechsler K, Wesarg S, Uder M, Janka R. Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images. J Digit Imaging. 2013;26:1082–90.

Zhao Z, Chen H, Wang L. A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge. Kidney and kidney. Tumor Seg. 2021;2021:13168.

Zhou L, Meng X, Huang Y, et al. An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors. Nat Mach Intell. 2022;4:494–503.

Guo D, Jin D, Zhu Z, Ho T-Y, Harrison AP, Chao C-H, et al. Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search. 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Seattle, WA, USA: IEEE; 2020. p. 4222–31. https://ieeexplore.ieee.org/document/9156960/ . Accessed 23 Nov 2022.

Chen Z, Li C, He J, Ye J, Song D, Wang S, et al. A Novel hybrid convolutional neural network for accurate organ segmentation in 3D head and neck CT images. arXiv; 2021. http://arxiv.org/abs/2109.12634 . Accessed 3 Oct 2022.

Chen Y, Ruan D, Xiao J, Wang L, Sun B, Saouaf R, et al. Fully automated multi-organ segmentation in abdominal magnetic resonance imaging with deep neural networks. Med Phys. 2020;47:4971–82.

Jain R, Sutradhar A, Dash AK, Das S. Automatic Multi-organ Segmentation on Abdominal CT scans using Deep U-Net Model. 2021 19th OITS international conference on information Technology (OCIT). Bhubaneswar, India: IEEE; 2021. p. 48–53. https://ieeexplore.ieee.org/document/9719516/ . Accessed 3 Oct 2022.

Ahn Y, Yoon JS, Lee SS, Suk H-I, Son JH, Sung YS, et al. Deep learning algorithm for automated segmentation and volume measurement of the liver and spleen using portal venous phase computed tomography images. Korean J Radiol. 2020;21:987.

Shi J, Wen K, Hao X, Xue X, An H, Zhang H. A Novel U-Like Network For The segmentation of thoracic organs. 2020 IEEE 17th international symposium on biomedical imaging workshops (ISBI Workshops). Iowa City, IA, USA: IEEE; 2020 [cited 2022 Oct 8]. p. 1–4. https://ieeexplore.ieee.org/document/9153358/ .

Pu Y, Kamata S-I, Wang Y. A Coarse to Fine Framework for Multi-organ Segmentation in Head and Neck Images. 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). Kitakyushu, Japan: IEEE; 2020 [cited 2022 Oct 3]. p. 1–6. https://ieeexplore.ieee.org/document/9306647/ .

Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys. 2017;44:5221–33.

Wang Y, Zhou Y, Shen W, Park S, Fishman EK, Yuille AL. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med Image Anal. 2019;55:88–102.

Tang H, Liu X, Han K, Sun S, Bai N, Chen X, et al. Spatial context-aware self-attention model for multi-organ segmentation. arXiv; 2020. http://arxiv.org/abs/2012.09279 . Accessed 3 Oct 2022.

Zhao Q, et al. Efficient multi-organ segmentation from 3D abdominal CT images with lightweight network and knowledge distillation. IEEE Trans Med Imaging. 2023;42:2513.

Cheng ZS, Zeng TY, Huang SJ, Yang X. A Novel Hybrid Network for H&N Organs at Risk Segmentation. Proceedings of the 2020 5th International Conference on Biomedical Signal and Image Processing. Suzhou China: ACM; 2020 [cited 2022 Oct 3]. p. 7–13. https://doi.org/10.1145/3417519.3417522

Zhou Y, Li Z, Bai S, Chen X, Han M, Wang C, et al. Prior-aware neural network for partially-supervised multi-organ segmentation. 2019 IEEE/CVF International conference on computer vision (ICCV). Seoul, Korea (South): IEEE; 2019. p. 10671–80. https://ieeexplore.ieee.org/document/9009566/ . Accessed 8 Oct 2022.

Lian S, Li L, Luo Z, Zhong Z, Wang B, Li S. Learning multi-organ segmentation via partial—and mutual-prior from single-organ datasets. Biomed Signal Process Control. 2023;80:104339.

Tappeiner E, Pröll S, Fritscher K, et al. Training of head and neck segmentation networks with shape prior on small datasets. Int J CARS. 2020;15:1417–25.

Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. 2020; https://arxiv.org/abs/2006.11239 . Accessed 23 Nov 2022.

Song J, Meng C, Ermon S. Denoising diffusion implicit models. 2020; https://arxiv.org/abs/2010.02502 . Accessed 23 Nov 2022.

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging. 2018;37:384–95.

Isler I, Lisle C, Rineer J, Kelly P, Turgut D, Ricci J, et al. Enhancing organ at risk segmentation with improved deep neural networks. arXiv; 2022. http://arxiv.org/abs/2202.01866 . Accessed 3 Oct 2022.

Vesal S, Ravikumar N, Maier A. A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT. 2019; https://arxiv.org/abs/1905.07710 . Accessed 8 Oct 2022.

Li, W., Wang, G., Fidon, L., Ourselin, S., Cardoso, M.J., Vercauteren, T. On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. Information processing in medical imaging IPMI. 2017; 10265.

Lin T-Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. 2017 IEEE conference on computer vision and pattern recognition (CVPR). Honolulu, HI: IEEE; 2017. p. 936–44. http://ieeexplore.ieee.org/document/8099589/ . Accessed 20 Mar 2022.

Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2018;40:834–48.

Srivastava A, Jha D, Keles E, Aydogan B, Abazeed M, Bagci U. An efficient multi-scale fusion network for 3D organ at Risk (OAR) Segmentation. 2022; https://arxiv.org/abs/2208.07417 . Accessed 3 Oct 2022.

Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, et al. Attention U-net: learning where to look for the pancreas. arXiv; 2018. http://arxiv.org/abs/1804.03999 . Accessed 20 Jun 2022.

Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell. 2020;42:2011–23.

Liu Z, Wang H, Lei W, Wang G. CSAF-CNN: Cross-Layer Spatial Attention Map Fusion Network for Organ-at-Risk Segmentation in Head and Neck CT Images. 2020 IEEE 17th international symposium on biomedical imaging (ISBI). Iowa City, IA, USA: IEEE; 2020. p. 1522–5. https://ieeexplore.ieee.org/document/9098711/ . Accessed 3 Oct 2022.

Lin H, Li Z, Yang Z, Wang Y. Variance-aware attention U-Net for multi-organ segmentation. Med Phys. 2021;48:7864–76.

Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J, et al. UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov D, Taylor Z, Carneiro G, Syeda-Mahmood T, Martel A, Maier-Hein L, et al., editors. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing; 2018. p. 3–11. https://doi.org/10.1007/978-3-030-00889-5_1 .

He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv:151203385 [cs]. 2015; http://arxiv.org/abs/1512.03385 . Accessed 14 Nov 2022.

Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, et al. Deformable convolutional networks. 2017 IEEE international conference on computer vision (ICCV). Venice: IEEE; 2017. p. 764–73. http://ieeexplore.ieee.org/document/8237351/ . Accessed 14 Nov 2022.

Heinrich MP, Oktay O, Bouteldja N. OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions. Med Image Anal. 2019;54:1–9.

Shen N, Wang Z, Li J, Gao H, Lu W, Hu P, et al. Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution. Expert Syst Appl. 2023;211:118625.

Hou Q, Zhang L, Cheng M-M, Feng J. Strip pooling: rethinking spatial pooling for scene parsing. 2020; https://arxiv.org/abs/2003.13328 . Accessed 17 Nov 2022.

Zhang F, Wang Y, Yang H. Efficient context-aware network for abdominal multi-organ segmentation. 2021; https://arxiv.org/abs/2109.10601 . Accessed 14 Nov 2022.

Jadon S. A survey of loss functions for semantic segmentation. 2020 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB). 2020. p. 1–7. http://arxiv.org/abs/2006.14822 . Accessed 3 Oct 2022.

Ma J, Chen J, Ng M, Huang R, Li Y, Li C, Yang X, Martel AL. Loss odyssey in medical image segmentation. Med Image Anal. 2021;71:102035.

Yi-de Ma, Qing Liu, Zhi-bai Quan. Automated image segmentation using improved PCNN model based on cross-entropy. Proceedings of 2004 international symposium on intelligent multimedia, video and speech processing, 2004. Hong Kong, China: IEEE; 2004. p. 743–6. http://ieeexplore.ieee.org/document/1434171/ . Accessed 3 Oct 2022.

Sudre CH, Li W, Vercauteren T, Ourselin S, Cardoso MJ. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. 2017. p. 240–8. http://arxiv.org/abs/1707.03237 . Accessed 3 Oct 2022.

Salehi SSM, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang Q, Shi Y, Suk H-I, Suzuki K, editors. Machine learning in medical imaging. Cham: Springer International Publishing; 2017. p. 379–87. https://doi.org/10.1007/978-3-319-67389-9_44 .

Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. 2017; https://arxiv.org/abs/1708.02002 . Accessed 3 Oct 2022.

Pihur V, Datta S, Datta S. Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach. Bioinformatics. 2007;23:1607–15.

Shen C, Roth HR, Oda H, Oda M, Hayashi Y, Misawa K, et al. On the influence of dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks. 2018; https://arxiv.org/abs/1801.05912 . Accessed 3 Oct 2022.

Tappeiner E, Welk M, Schubert R. Tackling the class imbalance problem of deep learning-based head and neck organ segmentation. Int J CARS. 2022;17:2103–11.

Kodym O, Španěl M, Herout A. Segmentation of head and neck organs at risk using CNN with batch dice loss. Pattern Recogn GCPR. 2018;2018:11269.

Abraham N, Khan NM. A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. arXiv; 2018. http://arxiv.org/abs/1810.07842 . Accessed 3 Oct 2022.

Wong KCL, Moradi M, Tang H, Syeda-Mahmood T. 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. 2018. p. 612–9. http://arxiv.org/abs/1809.00076 . Accessed 3 Oct 2022.

Song J, Chen X, Zhu Q, Shi F, Xiang D, Chen Z, et al. Global and local feature reconstruction for medical image segmentation. IEEE Trans Med Imaging. 2022;41:2273–84.

Lei W, Mei H, Sun Z, Ye S, Gu R, Wang H, et al. Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss. arXiv; 2021. http://arxiv.org/abs/2102.01897 . Accessed 3 Oct 2022.

Y. Song, J. Y. -C. Teoh, K. -S. Choi and J. Qin. Dynamic loss weighting for multiorgan segmentation in medical images. IEEE transactions on neural networks and learning systems.

Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, et al. The liver tumor segmentation benchmark (LiTS). 2019; https://arxiv.org/abs/1901.04056 . Accessed 19 Oct 2022.

Heller N, Sathianathen N, Kalapara A, Walczak E, Moore K, Kaluzniak H, et al. The KiTS19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes. 2019; https://arxiv.org/abs/1904.00445 . Accessed 19 Oct 2022.

Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, van Ginneken B, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. 2019; https://arxiv.org/abs/1902.09063 . Accessed 3 Oct 2022.

Chen S, Ma K, Zheng Y. Med3D: Transfer Learning for 3D Medical Image Analysis. arXiv:190400625 [cs]. 2019; http://arxiv.org/abs/1904.00625 . Accessed 20 Mar 2022.

Zhang J, Xie Y, Xia Y, Shen C. DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets. 2020; https://arxiv.org/abs/2011.10217 . Accessed 8 Oct 2022.

Xie Y, Zhang J, Xia Y, Shen C. Learning from partially labeled data for multi-organ and tumor segmentation. 2022; https://arxiv.org/abs/2211.06894 . Accessed 20 Nov 2022.

Dmitriev K, Kaufman AE. Learning multi-class segmentations from single-class datasets. 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Long Beach, CA, USA: IEEE; 2019. p. 9493–503. https://ieeexplore.ieee.org/document/8953428/ . Accessed 19 Oct 2022.

Wu H, Pang S, Sowmya A. Tgnet: a task-guided network architecture for multi-organ and tumour segmentation from partially labelled datasets. 2022 IEEE 19th international symposium on biomedical imaging (ISBI). Kolkata, India: IEEE; 2022. p. 1–5. https://ieeexplore.ieee.org/document/9761582/ . Accessed 7 Dec 2022.

Liu X, Wen B, Yang S. CCQ: cross-class query network for partially labeled organ segmentation. AAAI. 2023;37:1755–63.

Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, et al. Learning transferable visual models from natural language supervision. 2021: https://arxiv.org/abs/2103.00020 . Accessed 21 May 2023.

Liu J, Zhang Y, Chen J-N, Xiao J, Lu Y, Landman BA, et al. CLIP-driven universal model for organ segmentation and tumor detection. arXiv; 2023. http://arxiv.org/abs/2301.00785 . Accessed 21 May 2023.

Ye Y, Xie Y, Zhang J, Chen Z, Xia Y. UniSeg: a prompt-driven universal segmentation model as well as a strong representation learner. arXiv; 2023. http://arxiv.org/abs/2304.03493 . Accessed 21 May 2023.

Huang R, Zheng Y, Hu Z, Zhang S, Li H. Multi-organ segmentation via co-training weight-averaged models from few-organ datasets. 2020; https://arxiv.org/abs/2008.07149 . Accessed 8 Oct 2022.

Zhang L, Feng S, Wang Y, Wang Y, Zhang Y, Chen X, et al. Unsupervised ensemble distillation for multi-organ segmentation. 2022 IEEE 19th international symposium on biomedical imaging (ISBI). Kolkata, India: IEEE; 2022. p. 1–5. https://ieeexplore.ieee.org/document/9761568/ . Accessed 19 Oct 2022.

Liu H, Xu Z, Gao R, Li H, Wang J, Chabin G, et al. COSST: Multi-organ segmentation with partially labeled datasets using comprehensive supervisions and self-training. arXiv; 2023. http://arxiv.org/abs/2304.14030 . Accessed 18 Jul 2023.

Cermelli F, Mancini M, Bulò SR, Ricci E, Caputo B. Modeling the Background for incremental learning in semantic segmentation. 2020; https://arxiv.org/abs/2002.00718 . Accessed 21 May 2023.

Liu P, Xiao L, Zhou SK. Incremental learning for multi-organ segmentation with partially labeled datasets. 2021; https://arxiv.org/abs/2103.04526 . Accessed 15 Nov 2022.

Xu X, Yan P. Federated multi-organ segmentation with partially labeled data. 2022; https://arxiv.org/abs/2206.07156 . Accessed 15 Nov 2022.

Zhang Y, Li X, Chen H, Yuille A, Liu Y, Zhou Z. Continual learning for abdominal multi-organ and tumor segmentation. 2023; https://arxiv.org/abs/2306.00988 . Accessed 3 Aug 2023.

Ji Z, et al. Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans. IEEE/CVF Int Conf Comp Vis. 2023;2023:21083–94.

Fang X, Yan P. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans Med Imaging. 2020;39:3619–29.

Shi G, Xiao L, Chen Y, Zhou SK. Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Med Image Anal. 2021;70:101979.

Wu Q, Chen Y, Huang N, Yue X. Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator. Proceedings of the 2022 International Conference on Multimedia Retrieval. Newark NJ USA: ACM; 2022 [cited 2022 Nov 15]. p. 668–76. https://doi.org/10.1145/3512527.3531377 .

Kanavati F, Misawa K, Fujiwara M, Mori K, Rueckert D, Glocker B. Joint supervoxel classification forest for weakly-supervised organ segmentation. In: Wang Q, Shi Y, Suk H-I, Suzuki K, editors. Machine learning in medical imaging. Cham: Springer International Publishing; 2017. p. 79–87. https://doi.org/10.1007/978-3-319-67389-9_10 .

Bai W, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, et al. Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S, editors., et al., Medical image computing and computer-assisted intervention—MICCAI 2017. Cham: Springer International Publishing; 2017. p. 253–60. https://doi.org/10.1007/978-3-319-66185-8_29 .

Luo X, Hu M, Song T, Wang G, Zhang S. Semi-supervised medical image segmentation via cross teaching between CNN and transformer. 2021; https://arxiv.org/abs/2112.04894 . Accessed 7 Dec 2022.

Chen J, Zhang J, Debattista K, Han J. Semi-supervised unpaired medical image segmentation through task-affinity consistency. IEEE Trans Med Imaging. 2022. https://doi.org/10.1109/TMI.2022.3213372 .

Wu Y, Ge Z, Zhang D, Xu M, Zhang L, Xia Y, et al. Mutual consistency learning for semi-supervised medical image segmentation. Med Image Anal. 2022;81:102530.

Luo X, Liao W, Chen J, Song T, Chen Y, Zhang S, et al. Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, et al., editors. Medical image computing and computer assisted intervention—MICCAI 2021. Cham: Springer International Publishing; 2021. p. 318–29. https://doi.org/10.1007/978-3-030-87196-3_30 .

Jiao R, Zhang Y, Ding L, Cai R, Zhang J. Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. 2022; https://arxiv.org/abs/2207.14191 . Accessed 6 Dec 2022.

Zhou Y, Wang Y, Tang P, Bai S, Shen W, Fishman E, et al. Semi-Supervised 3D Abdominal multi-organ segmentation Via deep multi-planar co-training. 2019 IEEE winter conference on applications of computer vision (WACV). Waikoloa Village, HI, USA: IEEE; 2019. p. 121–40. https://ieeexplore.ieee.org/document/8658899/ . Accessed 8 Dec 2022.

Xia Y, Yang D, Yu Z, Liu F, Cai J, Yu L, et al. Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Med Image Anal. 2020;65:101766.

Lai H, Wang T, Zhou S. DLUNet: Semi-supervised learning based dual-light UNet for multi-organ segmentation. 2022. https://arxiv.org/abs/2209.10984 . Accessed 7 Dec 2022.

Chen D, Bai Y, Shen W, Li Q, Yu L, Wang Y. MagicNet: semi-supervised multi-organ segmentation via magic-cube partition and recovery. 2023; https://doi.org/10.48550/arXiv.2212.14310 .

Lee HH, Tang Y, Tang O, Xu Y, Chen Y, Gao D, et al. Semi-supervised multi-organ segmentation through quality assurance supervision. In: Landman BA, Išgum I. (Eds). Medical Imaging 2020: Image Processing [Internet]. Houston, United States: SPIE; 2020 [cited 2022 Nov 15]. p. 53. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/2549033/Semi-supervised-multi-organ-segmentation-through-quality-assurance-supervision/ https://doi.org/10.1117/12.2549033.full

Raju A, Cheng C-T, Huo Y, Cai J, Huang J, Xiao J, et al. Co-heterogeneous and adaptive segmentation from multi-source and multi-phase ct imaging data: a study on pathological liver and lesion segmentation. In: Vedaldi A, Bischof H, Brox T, Frahm J-M, editors., et al., Computer vision—ECCV 2020. Cham: Springer International Publishing; 2020. p. 448–65. https://doi.org/10.1007/978-3-030-58592-1_27 .

Litjens G, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv:231200752.

Wang Z, Zheng J-Q, Zhang Y, Cui G, Li L. Mamba-UNet: UNet-Like pure visual mamba for medical image segmentation. arXiv:240205079. 2024.

Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal. 2020;63:101693.

Qu L, Liu S, Liu X, Wang M, Song Z. Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis. arXiv; 2022. http://arxiv.org/abs/2208.08789 . Accessed 19 Aug 2022.

Qin Y, et al. Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT. IEEE Trans Med Imaging. 2021;40:1603–17.

Cerrolaza JJ, Picazo ML, Humbert L, Sato Y, Rueckert D, Ballester MA, Linguraru MG. Computational anatomy for multi-organ analysis in medical imaging: a review. Med Image Anal. 2019;56:44–67.

Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language models are few-shot learners. arXiv; 2020. http://arxiv.org/abs/2005.14165 . Accessed 21 May 2023.

Ma J, He Y, Li F, Han L, You C, Wang B. Segment anything in medical images. Nat Commun. 2023;15:654. https://doi.org/10.48550/arXiv.2304.12306 .

Zhao Z et al. CLIP in medical imaging: a comprehensive survey. arXiv:231207353. 2023.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant 82072021. This work was also supported by the medical–industrial integration project of Fudan University under grant XM03211181.

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Xiaoyu Liu and Linhao Qu have contributed equally to this article.

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Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People’s Republic of China

Xiaoyu Liu, Linhao Qu, Ziyue Xie, Jiayue Zhao, Yonghong Shi & Zhijian Song

Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China

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Xiaoyu Liu and Linhao Qu conceived and wrote the manuscript. Ziyue Xie, Jiayue Zhao, Yonghong Shi, Zhijian Song revising manuscript critically for important intellectual content. All authors read and approved the final manuscript.

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Liu, X., Qu, L., Xie, Z. et al. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. BioMed Eng OnLine 23 , 52 (2024). https://doi.org/10.1186/s12938-024-01238-8

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DOI : https://doi.org/10.1186/s12938-024-01238-8

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