A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

The article presents a comprehensive survey of medical image segmentation, reviewing key methods based on U-Net, Transformer, and SAM architectures, along with their associated public datasets and evaluation metrics. It highlights the challenges faced in the field and organizes these methods within a unified framework to enhance segmentation accuracy and efficiency. This work is significant for practitioners as it provides a structured overview of current approaches and resources, facilitating future research and clinical applications in medical image segmentation.