Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.

翻译：深度学习技术在推进医学图像分析方面具有巨大潜力，尤其是在图像分割等任务中。医学图像中对感兴趣区域或体积的精确标注至关重要，但手动标注不仅费时费力，且易受观察者间及观察者自身偏差的影响。因此，深度学习可为这类应用提供自动化解决方案。然而，这些技术的潜力常因可复现性与泛化性方面的挑战而受限，这亦是其临床转化的主要障碍。本文介绍RIDGE清单，这是一个旨在评估基于深度学习的医学图像分割模型的可复现性、完整性、可靠性、泛化性与效率的综合性框架。RIDGE清单不仅是一个评估工具，也是研究人员致力于提升其工作质量与透明度的指南。遵循RIDGE清单所概述的原则，研究人员可确保其开发的分割模型具有鲁棒性、科学有效性，并适用于临床环境。