Maleki Farhad, Moy Linda, Forghani Reza, Ghosh Tapotosh, Ovens Katie, Langer Steve, Rouzrokh Pouria, Khosravi Bardia, Ganjizadeh Ali, Warren Daniel, Daneshjou Roxana, Moassefi Mana, Avval Atlas Haddadi, Sotardi Susan, Tenenholtz Neil, Kitamura Felipe, Kline Timothy
Department of Computer Science, University of Calgary, Calgary, AB, Canada.
Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada.
J Imaging Inform Med. 2024 Nov 18. doi: 10.1007/s10278-024-01282-9.
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.
深度学习技术在推进医学图像分析方面具有巨大潜力，特别是在图像分割等任务中，医学图像中感兴趣区域或体积的精确标注至关重要，但人工操作既费力又容易出现观察者间和观察者内的偏差。因此，深度学习方法可以为这类应用提供自动化解决方案。然而，这些技术的潜力常常受到可重复性和泛化性挑战的影响，而这些挑战是它们在临床应用中的关键障碍。本文介绍了RIDGE清单，这是一个全面的框架，旨在评估基于深度学习的医学图像分割模型的可重复性、完整性、可靠性、泛化性和效率。RIDGE清单不仅是一种评估工具，也是研究人员努力提高其工作质量和透明度的指南。通过遵循RIDGE清单中概述的原则，研究人员可以确保他们开发的分割模型是稳健的、科学有效的，并且适用于临床环境。
