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.

翻译：医学图像分割在临床诊断、治疗规划、疾病监测及神经系统疾病识别中发挥着关键作用。本文对其系统性发展进行了全面综述，涵盖了广泛使用的公开数据集、基于U-Net、Transformer和SAM架构的代表性方法及其关键差异，以及主要评估指标，并从多角度分析了关键挑战。与聚焦单一模型系列或特定临床应用的综述不同，本综述将基于U-Net、Transformer和SAM的方法纳入统一的分析框架，重点关注它们在提高分割准确性和效率方面的有效性。本研究旨在指导未来研究并支持医学图像分割的临床转化，所有相关资源已公开于我们的GitHub仓库：https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main。