Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks
This work addresses the need for reliable TKV measurement in ADPKD patients, which is crucial for clinical prognosis, though it appears incremental by improving segmentation accuracy with a multi-task 3D CNN approach.
The paper tackled the problem of accurately segmenting kidneys in Autosomal Dominant Polycystic Kidney Disease (ADPKD) from CT images to compute Total Kidney Volume (TKV), achieving a mean DICE score of 0.95 and a mean absolute percentage TKV error of 3.86.
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.