Evaluating Medical Image Segmentation Models: A Critical Assessment of Existing Methods in Clinical Contexts

Despite the rapid development of medical image segmentation models, a significant gap persists in the availability of specific evaluation methods tailored to the nuances of clinical application scenarios. Current methods often fall short in terms of comprehensiveness, complexity, and consistency. This article critically examines these limitations, emphasizing the need for more sophisticated evaluation frameworks to adequately assess the performance of medical image segmentation models in real-world clinical contexts.