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The thrombotic microangiopathies (TMAs) manifest in renal biopsy histologywith a broad spectrum of acute and chronic findings. Precise diagnosticcriteria for a renal biopsy diagnosis of TMA are missing. As a first steptowards a machine learning- and computer vision-based analysis of wholes slideimages from renal biopsies, we trained a segmentation model for the decisivediagnostic kidney tissue compartments artery, arteriole, glomerulus on a set ofwhole slide images from renal biopsies with TMAs and Mimickers (distinctdiseases with a similar nephropathological appearance as TMA like severe benignnephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy,arteriolar light chain deposition disease). Our segmentation model combines aU-Net-based tissue detection with a Shifted windows-transformer architecture toreach excellent segmentation results for even the most severely alteredglomeruli, arterioles and arteries, even on unseen staining domains from adifferent nephropathology lab. With accurate automatic segmentation of thedecisive renal biopsy compartments in human renal vasculopathies, we have laidthe foundation for large-scale compartment-specific machine learning andcomputer vision analysis of renal biopsy repositories with TMAs.

Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)