Foundation Models for Biomedical Image Segmentation: A Survey
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913197011763200 |
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| author | Lee, Ho Hin Gu, Yu Zhao, Theodore Xu, Yanbo Yang, Jianwei Usuyama, Naoto Wong, Cliff Wei, Mu Landman, Bennett A. Huo, Yuankai Santamaria-Pang, Alberto Poon, Hoifung |
| author_facet | Lee, Ho Hin Gu, Yu Zhao, Theodore Xu, Yanbo Yang, Jianwei Usuyama, Naoto Wong, Cliff Wei, Mu Landman, Bennett A. Huo, Yuankai Santamaria-Pang, Alberto Poon, Hoifung |
| contents | Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing. Within the last year, marked by over 100 publications, SAM has demonstrated its prowess in zero-shot learning adaptations for medical imaging. The fundamental premise of SAM lies in its capability to segment or identify objects in images without prior knowledge of the object type or imaging modality. This approach aligns well with tasks achievable by the human visual system, though its application in non-biological vision contexts remains more theoretically challenging. A notable feature of SAM is its ability to adjust segmentation according to a specified resolution scale or area of interest, akin to semantic priming. This adaptability has spurred a wave of creativity and innovation in applying SAM to medical imaging. Our review focuses on the period from April 1, 2023, to September 30, 2023, a critical first six months post-initial publication. We examine the adaptations and integrations of SAM necessary to address longstanding clinical challenges, particularly in the context of 33 open datasets covered in our analysis. While SAM approaches or achieves state-of-the-art performance in numerous applications, it falls short in certain areas, such as segmentation of the carotid artery, adrenal glands, optic nerve, and mandible bone. Our survey delves into the innovative techniques where SAM's foundational approach excels and explores the core concepts in translating and applying these models effectively in diverse medical imaging scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_07654 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Foundation Models for Biomedical Image Segmentation: A Survey Lee, Ho Hin Gu, Yu Zhao, Theodore Xu, Yanbo Yang, Jianwei Usuyama, Naoto Wong, Cliff Wei, Mu Landman, Bennett A. Huo, Yuankai Santamaria-Pang, Alberto Poon, Hoifung Computer Vision and Pattern Recognition Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing. Within the last year, marked by over 100 publications, SAM has demonstrated its prowess in zero-shot learning adaptations for medical imaging. The fundamental premise of SAM lies in its capability to segment or identify objects in images without prior knowledge of the object type or imaging modality. This approach aligns well with tasks achievable by the human visual system, though its application in non-biological vision contexts remains more theoretically challenging. A notable feature of SAM is its ability to adjust segmentation according to a specified resolution scale or area of interest, akin to semantic priming. This adaptability has spurred a wave of creativity and innovation in applying SAM to medical imaging. Our review focuses on the period from April 1, 2023, to September 30, 2023, a critical first six months post-initial publication. We examine the adaptations and integrations of SAM necessary to address longstanding clinical challenges, particularly in the context of 33 open datasets covered in our analysis. While SAM approaches or achieves state-of-the-art performance in numerous applications, it falls short in certain areas, such as segmentation of the carotid artery, adrenal glands, optic nerve, and mandible bone. Our survey delves into the innovative techniques where SAM's foundational approach excels and explores the core concepts in translating and applying these models effectively in diverse medical imaging scenarios. |
| title | Foundation Models for Biomedical Image Segmentation: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07654 |