  Glomerulosclerosis, interstitial fibrosis, and tubular atrophy (IFTA) are
histologic indicators of irrecoverable kidney injury. In standard clinical
practice, the renal pathologist visually assesses, under the microscope, the
percentage of sclerotic glomeruli and the percentage of renal cortical
involvement by IFTA. Estimation of IFTA is a subjective process due to a varied
spectrum and definition of morphological manifestations. Modern artificial
intelligence and computer vision algorithms have the ability to reduce
inter-observer variability through rigorous quantitation. In this work, we
apply convolutional neural networks for the segmentation of glomerulosclerosis
and IFTA in periodic acid-Schiff stained renal biopsies. The convolutional
network approach achieves high performance in intra-institutional holdout data,
and achieves moderate performance in inter-intuitional holdout data, which the
network had never seen in training. The convolutional approach demonstrated
interesting properties, such as learning to predict regions better than the
provided ground truth as well as developing its own conceptualization of
segmental sclerosis. Subsequent estimations of IFTA and glomerulosclerosis
percentages showed high correlation with ground truth.
