Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks

2018/09/07 by Deepak Keshwani, Keshwani, Deepak, Yoshiro Kitamura +3
Biochemistry, Genetics and Molecular Biology · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Genetic and Kidney Cyst Diseases #Renal and Vascular Pathologies #Renal cell carcinoma treatment

paper · pdf · doi:10.48550/arxiv.1809.02268

openalex publication_date 2018/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by progressive growth of renal cysts is the most prevalent and potentially lethal monogenic renal disease, affecting one in every 500-100 people. Total Kidney Volume (TKV) and its growth computed from Computed Tomography images has been accepted as an essential prognostic marker for renal function loss. Due to large variation in shape and size of kidney in ADPKD, existing methods to compute TKV (i.e. to segment ADKP) including those based on 2D convolutional neural networks are not accurate enough to be directly useful in clinical practice. In this work, we propose multi-task 3D Convolutional Neural Networks to segment ADPK and achieve a mean DICE score of 0.95 and mean absolute percentage TKV error of 3.86. Additionally, to solve the challenge of class imbalance, we propose to simply bootstrap cross entropy loss and compare results with recently prevalent dice loss in medical image segmentation community.

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