Advantages of transformer and its application for medical image segmentation: a survey Article Swipe
Qiumei Pu
,
Zuoxin Xi
,
Shuai Yin
,
Zhe Zhao
,
Lina Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12938-024-01212-4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12938-024-01212-4
Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12938-024-01212-4
- https://biomedical-engineering-online.biomedcentral.com/counter/pdf/10.1186/s12938-024-01212-4
- OA Status
- gold
- Cited By
- 71
- References
- 91
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391514919
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391514919Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12938-024-01212-4Digital Object Identifier
- Title
-
Advantages of transformer and its application for medical image segmentation: a surveyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-03Full publication date if available
- Authors
-
Qiumei Pu, Zuoxin Xi, Shuai Yin, Zhe Zhao, Lina ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1186/s12938-024-01212-4Publisher landing page
- PDF URL
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https://biomedical-engineering-online.biomedcentral.com/counter/pdf/10.1186/s12938-024-01212-4Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://biomedical-engineering-online.biomedcentral.com/counter/pdf/10.1186/s12938-024-01212-4Direct OA link when available
- Concepts
-
Computer science, Segmentation, Transformer, Artificial intelligence, Image segmentation, Convolutional neural network, Computer vision, Pattern recognition (psychology), Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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71Total citation count in OpenAlex
- Citations by year (recent)
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2025: 57, 2024: 14Per-year citation counts (last 5 years)
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91Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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