We developed a digital pipeline to classify renal biopsies from patients with Diabetic Nephropathy (DN). We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification.
B. Ginley, B. Lutnick, K. Y. Jen, A. Fogo, S. Jain, A. Rosenberg, V. Walavalkar, G. Wilding, J. E. Tomaszewski, R. Yacoub, G. M. Rossi, and P. Sarder, “Computational segmentation and classification of diabetic glomerulosclerosis,” Journal of the American Society of Nephrology, vol. 30, no. 10, pp. 1953-67, Oct. 2019.