Size Matters: Automated Quantification of Acute Changes in Kidney Size with Deep Dilated U-Net Segmentation of Dynamic Parametric MRI

Deep learning algorithms enable fast kidney segmentation, which is crucial to establish renal size as a (pre)clinical biomarker for renal diseases. Tackling this challenge, a novel deep dilated U-Net (DDU-Net) was trained, validated, and tested on preclinical ground truth data, benchmarked on simulated data against an analytical model, and applied to longitudinal in vivo MRI scans acquired in rats, with pathophysiological interventions mimicking clinically realistic scenarios. Our DDU-Net reached a Dice score of 0.98 on the ground truth, outperformed the analytical approach, and facilitated rapid detection of acute changes in kidney size upon acute pathophysiological interventions.