Abstract #3625

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

Tobias Klein^1,2, Thomas Gladytz¹, Jason M. Millward¹, Kathleen Cantow³, Luis Hummel³, Erdmann Seeliger³, Sonia Waiczies¹, Christoph Lippert^2,4, and Thoralf Niendorf^1,5

¹Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Berlin, Germany, ²Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany, ³Institute of Translational Physiology, Charité - Universitätsmedizin, Berlin, Germany, ⁴Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ⁵Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany

Synopsis

Keywords: Kidney, Preclinical

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.

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