Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey
It addresses the challenge of adapting prompt-driven segmentation models to medical images, which is incremental as it summarizes existing efforts rather than proposing new methods.
This survey examines the applicability of the Segment Anything Model (SAM) to medical image segmentation, finding that direct application yields unsatisfactory performance on multi-modal and multi-target datasets, but provides insights for developing foundation models in medical analysis.
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 work, 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. Although directly applying SAM to medical image segmentation cannot obtain satisfying performance on multi-modal and multi-target medical datasets, many insights are drawn to guide future research to develop foundation models for medical image analysis. To facilitate future research, we maintain an active repository that contains up-to-date paper list and open-source project summary at https://github.com/YichiZhang98/SAM4MIS.