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Due to the inherent flexibility of prompting, foundation models have emergedas the predominant force in the fields of natural language processing andcomputer vision. The recent introduction of the Segment Anything Model (SAM)signifies a noteworthy expansion of the prompt-driven paradigm into the domainof image segmentation, thereby introducing a plethora of previously unexploredcapabilities. However, the viability of its application to medical imagesegmentation remains uncertain, given the substantial distinctions betweennatural and medical images. In this work, we provide a comprehensive overviewof recent endeavors aimed at extending the efficacy of SAM to medical imagesegmentation tasks, encompassing both empirical benchmarking and methodologicaladaptations. Additionally, we explore potential avenues for future researchdirections in SAM's role within medical image segmentation. While directapplication of SAM to medical image segmentation does not yield satisfactoryperformance on multi-modal and multi-target medical datasets so far, numerousinsights gleaned from these efforts serve as valuable guidance for shaping thetrajectory of foundational models in the realm of medical image analysis. Tosupport ongoing research endeavors, we maintain an active repository thatcontains an up-to-date paper list and a succinct summary of open-sourceprojects at https://github.com/YichiZhang98/SAM4MIS.

Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions