How Segment Anything Model (SAM) Boost Medical Image Segmentation?
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Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introduction of the Segment Anything Model (SAM), the prompt-driven paradigm has entered the realm of image segmentation, bringing with a range of previously unexplored capabilities. However, it remains unclear whether it can be applicable to medical image segmentation due to the significant differences between natural images and medical images. In this report, we summarize recent efforts to extend the success of SAM to medical image segmentation tasks, including both empirical benchmarking and methodological adaptations, and discuss potential future directions for SAM in medical image segmentation. We also set up a collection of literature reviews to boost the research on this topic at https://github.com/YichiZhang98/SAM4MIS.
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