Deep learning-based segmentation and quantification in experimental kidney histopathology

N Bouteldja, BM Klinkhammer, RD Bülow, P Droste, SW Otten, SF von Stillfried, J Moellmann, SM Sheehan, R Korstanje, S Menzel, P Bankhead, M Mietsch, C Drummer, M Lehrke, Rafael Kramann, J Floege, P Boor, D Merhof

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)

Abstract

Background Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation. Methods We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman’s capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total. Results Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research—including rats, pigs, bears, and marmosets—as well as in humans, providing a translational bridge between preclinical and clinical studies. Conclusions We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.

Original languageEnglish
Pages (from-to)52-68
Number of pages17
JournalJournal of the American Society of Nephrology
Volume32
Issue number1
DOIs
Publication statusPublished - Jan 2021

Bibliographical note

Funding Information:
P. Bankhead reports other from Philips Digital Pathology Solutions, outside the submitted work; and is the primary inventor and maintainer of the QuPath open source software platform. J. Floege reports other from Amgen, Bayer, Calliditas, Fresenius, Omeros, Retrophin, and Vifor, outside the submitted work. R. Kramann reports grants from Chugai, outside the submitted work. M. Lehrke reports consultancy agreements with Amgen, Bayer, Boehringer Ingelheim, Lilly, MSD, Novartis, and Novo Nordisk; research funding from Boehringer Ingelheim, MSD, and Novo Nordisk; honoraria from Amgen, Bayer, Boehringer Ingelheim, Lilly, MSD, Novartis, and Novo Nordisk; and being a scientific advisor to or membership with Amgen, Bayer, Boehringer Ingelheim, Lilly, MSD, Novartis, and Novo Nordisk. All remaining authors have nothing to disclose.

Publisher Copyright:
Copyright © 2021 by the American Society of Nephrology.

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