Segment Anything Model for medical image segmentation: A review.
Authors
Affiliations (2)
Affiliations (2)
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing, 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots, Chongqing, 401135, China; Key Laboratory of Advanced Equipment Intelligence of Chongqing Education Commission of China, Chongqing, 401135, China. Electronic address: [email protected].
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing, 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots, Chongqing, 401135, China; Key Laboratory of Advanced Equipment Intelligence of Chongqing Education Commission of China, Chongqing, 401135, China.
Abstract
The Segment Anything Model (SAM) is the first foundational model in general image segmentation, exhibiting outstanding generalization capability across a wide range of natural image tasks. However, medical images differ fundamentally from natural images in modality characteristics, semantic structure, and clinical objectives, resulting in limited systematic research on SAM adaptation to medical imaging. To bridge this gap and clarify the key challenges and developmental pathways for transferring general segmentation models to the medical domain, this review provides a comprehensive overview of recent advances and optimization strategies of SAM and its variants in medical image segmentation. Specifically, we first introduce the core principles of SAM and its research background in medical imaging, outlining the major technical challenges encountered in clinical contexts. Through an in-depth analysis and comparison of existing studies, we identify five key challenges, including the data gap, dimensionality barrier, precision bottleneck, semantic disconnect, and deployment hurdles. We then construct an integrated analytical framework to evaluate current adaptation mechanisms and optimization strategies. Furthermore, we discuss the potential and limitations of representative methods in clinical diagnosis, lesion detection, and surgical planning, summarizing the primary directions for improving model trustworthiness, interpretability, and privacy compliance. Finally, we highlight current research limitations and outline promising future directions, including the bidirectional integration of large language models, causal representation learning, and prompt optimization. We hope this work will serve as a valuable reference for future SAM-based medical image segmentation research and promote progress toward cross-modal integration, trustworthy artificial intelligence, and clinically deployable intelligent segmentation systems.