Preclinical animal experiments are of high importance in nephrology research, with histology as a major readout. Here, the authors provide a multiclass histology segmentation tool to evaluate animal kidney disease models using deep learning. A convolutional neural network (CNN) enabled a rapid, automated, high-performance whole slide segmentation of renal histology, allowing high-throughput analyses in various species and multiple murine disease models. The CNN also showed high performance in patient samples, providing a translational bridge between preclinical and clinical research. Extracted quantitative morphological features closely correlated with standard morphometric measurements. In conclusion, deep learning-based segmentation in experimental renal pathology opens new dimensions of reproducible,
unbiased and high-throughput quantitative digital nephropathology.