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

TL;DR

This paper introduces a multi-task 3D CNN approach for accurately segmenting kidneys in CT images of ADPKD patients, enabling precise TKV computation crucial for prognosis.

Contribution

It presents a novel multi-task 3D CNN model that improves kidney segmentation accuracy and addresses class imbalance with a bootstrap cross entropy loss.

Findings

Achieved a mean DICE score of 0.95 in kidney segmentation.

Attained a mean absolute percentage TKV error of 3.86%.

Demonstrated effectiveness of bootstrap cross entropy loss over dice loss.

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.