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Glomerulosclerosis, interstitial fibrosis, and tubular atrophy (IFTA) arehistologic indicators of irrecoverable kidney injury. In standard clinicalpractice, the renal pathologist visually assesses, under the microscope, thepercentage of sclerotic glomeruli and the percentage of renal corticalinvolvement by IFTA. Estimation of IFTA is a subjective process due to a variedspectrum and definition of morphological manifestations. Modern artificialintelligence and computer vision algorithms have the ability to reduceinter-observer variability through rigorous quantitation. In this work, weapply convolutional neural networks for the segmentation of glomerulosclerosisand IFTA in periodic acid-Schiff stained renal biopsies. The convolutionalnetwork approach achieves high performance in intra-institutional holdout data,and achieves moderate performance in inter-intuitional holdout data, which thenetwork had never seen in training. The convolutional approach demonstratedinteresting properties, such as learning to predict regions better than theprovided ground truth as well as developing its own conceptualization ofsegmental sclerosis. Subsequent estimations of IFTA and glomerulosclerosispercentages showed high correlation with ground truth.

Neural Network Segmentation of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis in Renal Biopsies