Deep learning techniques, despite their potential, often suffer from a lack of reproducibility and generalizability, impeding their clinical adoption. Image segmentation is one of the critical tasks in medical image analysis, in which one or several regions/volumes of interest should be annotated. This paper introduces the RIDGE checklist, a framework for assessing the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The checklist serves as a guide for researchers to enhance the quality and transparency of their work, ensuring that segmentation models are not only scientifically sound but also clinically relevant.

介绍了RIDec列表，这是一种评估基于深度学习的医学图像分割模型可重复性、完整性、可靠性、泛化能力和效率的框架，该检查列表为研究人员提供指导，以提高他们工作的质量和透明度，确保分割模型不仅在科学上可靠，而且在临床上有意义。

RIDGE：医学图像分割模型的再现性、完整性、可靠性、泛化能力及效率评估