A Critical Review of Evaluation Techniques for U-shaped Neural Networks in Medical Image Segmentation

U-shaped neural networks, characterized by their encoder and decoder structures, have emerged as a dominant force in medical image segmentation. While substantial research has focused on advancing the architectures of these models, the development of robust and clinically applicable evaluation techniques has lagged behind. Current evaluation methodologies are often complex and yield results that are difficult to interpret in a clinical context. This complexity hinders the translation of promising models into practical clinical workflows. Moreover, the field lacks a standardized and comprehensive framework for evaluating these models, leading to inconsistencies and difficulties in comparing results across different studies. Further research is urgently needed to develop evaluation metrics and techniques that are not only statistically sound but also clinically meaningful and interpretable, ultimately facilitating the safe and effective integration of these powerful models into clinical practice.