  We demonstrate automated segmentation of clear cell renal cell carcinomas
(ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von
Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on
Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3
patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to
2021. Lesions were manually segmented on T1 excretory phase, co-registered on
all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net
performance was evaluated on 10 randomized splits of the cohort. The models
were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net
achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88
and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts,
and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion
detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts,
and tumors, respectively. We demonstrated good detection and moderate
segmentation results using U-Net for ccRCC on MRI. Automatic detection and
segmentation of normal renal parenchyma, cysts, and masses may assist
radiologists in quantifying the burden of disease in patients with VHL.
