Organizations: School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China · SPIC Digital Technology Co., Ltd, Beijing, 102209, China · Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China · Department 6 of Health Care, Second Medical Center, People’s Liberation Army General Hospital, Beijing, 100037, China
Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U-Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical application, this review organizes U-Net-, Transformer-, and SAM-based methods within a unified analytical framework, with a particular focus on their effectiveness in improving segmentation accuracy and efficiency. This work aims to guide future research and support clinical translation of medical image segmentation, with all related resources publicly available in our GitHub repository: https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main.