Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework

TL;DR

This study develops and validates a robust AI-based kidney abnormality segmentation algorithm using publicly available data, demonstrating high accuracy and generalizability across diverse datasets and patient subgroups.

Contribution

The paper introduces a thoroughly validated kidney segmentation framework trained solely on public data, outperforming existing models and ensuring robustness for clinical and research applications.

Findings

Effective generalization to external datasets

Outperforms existing state-of-the-art models

Maintains high performance across patient subgroups

Abstract

Kidney abnormality segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and abnormalities, including tumors and cysts, which are typically staged based on diameter, volume, and anatomical location. To support a more objective and reproducible approach, this research aims to develop a robust, thoroughly validated kidney abnormality segmentation algorithm, made publicly available for clinical and research use. We employ publicly available training datasets and leverage the state-of-the-art medical image segmentation framework nnU-Net. Validation is conducted using both proprietary and public test datasets, with segmentation performance quantified by Dice coefficient and the 95th percentile Hausdorff distance. Furthermore, we analyze robustness across subgroups based on patient sex, age, CT contrast phases, and tumor histologic subtypes. Our findings demonstrate that our segmentation algorithm, trained exclusively on publicly available data, generalizes effectively to external test sets and outperforms existing state-of-the-art models across all tested datasets. Subgroup analyses reveal consistent high performance, indicating strong robustness and reliability. The developed algorithm and associated code are publicly accessible at https://github.com/DIAGNijmegen/oncology-kidney-abnormality-segmentation.