  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.

Computer Vision and Pattern Recognition