Deep Learning–Based Segmentation and Quantification in Experimental Kidney Histopathology

Significance Statement Nephropathologic analyses provide important outcomes-related data in the animal model studies that are essential to understanding kidney disease pathophysiology. In this work, the authors used a deep learning technique, the convolutional neural network, as a multiclass histology segmentation tool to evaluate kidney disease in animal models. This enabled a rapid, automated, high-performance segmentation of digital whole-slide images of periodic acid–Schiff–stained kidney tissues, allowing high-throughput quantitative and comparative analyses in multiple murine disease models and other species. The convolutional neural network also performed well in evaluating patient samples, providing a translational bridge between preclinical and clinical research. Extracted quantitative morphologic features closely correlated with standard morphometric measurements. Deep learning–based segmentation in experimental renal pathology is a promising step toward reproducible, unbiased, and high-throughput quantitative digital nephropathology. 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.