系统梳理医学图像分割的挑战、数据集与主流方法，助力临床应用落地。
A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond
- 按U-Net、Transformer、SAM架构统一分析主流方法
- 对比不同模型在精度与效率上的表现差异
- 适合关注医学图像分割研究与临床转化的读者
医学图像分割在临床诊断、治疗规划、疾病监测和神经精神障碍识别中起关键作用。本文系统回顾了该领域的进展，涵盖常用公开数据集、基于U-Net、Transformer和SAM架构的代表性方法，以及核心评估指标及其差异，并从多角度剖析主要挑战。不同于仅聚焦单一模型或特定临床应用的综述，本文将U-Net、Transformer、SAM类方法纳入统一分析框架，重点探讨其在提升分割精度与效率方面的有效性。本工作旨在引导未来研究并推动医学图像分割的临床转化，所有相关资源均公开于GitHub仓库：https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main。
原文摘要 · Abstract (English)
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.
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