A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond
For researchers and clinicians, this provides a structured overview of segmentation methods and challenges, but it is a survey with no novel technical contribution.
This survey reviews medical image segmentation, covering datasets, U-Net, Transformer, and SAM methods, and evaluation metrics. It organizes these methods in a unified framework to guide future research and clinical translation.
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.