A New 3D CNN-based CAD System for Early Detection of Acute Renal Transplant Rejection

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant function. In this paper, a computer-aided diagnostic (CAD) system based on a 3D convolutional neural network (CNN) is developed to assess renal transplant functionality using diffusion MRI-derived markers extracted from (3D + b-value) DW-MRI. Our framework performs the following image processing steps: (i) 3D DW-MRI kidney segmentation using a level-set approach guided by shape and visual appearance features; (ii) feature extraction, namely, voxel-wise apparent diffusion coefficients (3D ADCs) of the segmented DW-MRIs are estimated at different b-values (i.e. gradient field strengths and duration); and (iii) renal transplant status classification, while the extracted 3D ADCs are used as input to train and test a 3D CNN-based classifier to evaluate renal transplant status. The proposed CAD system achieved a 94% accuracy, a 94% sensitivity, and a 94% specificity using a leave-one-subject-out cross-validation scenario in distinguishing non-rejection (NR) from acute rejection (AR) renal transplants. These preliminary results hold a strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.