A Promising Non-invasive CAD System for Kidney Function Assessment

• Published in: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 Vol. 9902; pp. 613 - 621
• Main Authors: Shehata, M., Khalifa, F., Soliman, A., El-Ghar, M. Abou, Dwyer, A., Gimel’farb, G., Keynton, R., El-Baz, A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Clinical biomarkers; Deep learning; DW-MRI; Kidney diseases
• ISBN: 9783319467252; 3319467255
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46726-9_71

This paper introduces a novel computer-aided diagnostic (CAD) system for the assessment of renal transplant status that integrates image-based biomarkers derived from 4D (3D + b-value) diffusion-weighted (DW) MRI, and clinical biomarkers. To analyze DW-MRI, our framework starts with kidney tissue segmentation using a level set approach after DW-MRI data alignment to handle the motion effects. Secondly, the cumulative empirical distributions (i.e., CDFs) of apparent diffusion coefficients (ADCs) of the segmented DW-MRIs are estimated at low and high gradient strengths and duration (b-values) accounting for both blood perfusion and diffusion, respectively. Finally, these CDFs are fused with laboratory-based biomarkers (creatinine clearance and serum plasma creatinine) for the classification of transplant status using a deep learning-based classification approach utilizing a stacked non-negativity constrained auto-encoder. Using “leave-one-subject-out” experiments on a cohort of 58 subjects, the proposed CAD system distinguished non-rejection transplants from kidneys with abnormalities with a 95 % accuracy (sensitivity = 95 %, specificity = 94 %) and achieved a 95 % correct classification between early rejection and other kidney diseases. Our preliminary results demonstrate the promise of the proposed CAD system as a reliable non-invasive diagnostic tool for renal transplants assessment.