RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and
  Efficiency Assessment of Medical Image Segmentation Models
Farhad Maleki Linda Moy Reza Forghani Tapotosh Ghosh Katie Ovens Steve Langer Pouria Rouzrokh Bardia Khosravi Ali Ganjizadeh Daniel Warren Roxana Daneshjou Mana Moassefi Atlas Haddadi Avval Susan Sotardi Neil Tenenholtz Felipe Kitamura Timothy Kline
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.