The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology
with a broad spectrum of acute and chronic findings. Precise diagnostic
criteria for a renal biopsy diagnosis of TMA are missing. As a first step
towards a machine learning- and computer vision-based analysis of wholes slide
images from renal biopsies, we trained a segmentation model for the decisive
diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of
whole slide images from renal biopsies with TMAs and Mimickers (distinct
diseases with a similar nephropathological appearance as TMA like severe benign
nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy,
arteriolar light chain deposition disease). Our segmentation model combines a
U-Net-based tissue detection with a Shifted windows-transformer architecture to
reach excellent segmentation results for even the most severely altered
glomeruli, arterioles and arteries, even on unseen staining domains from a
different nephropathology lab. With accurate automatic segmentation of the
decisive renal biopsy compartments in human renal vasculopathies, we have laid
the foundation for large-scale compartment-specific machine learning and
computer vision analysis of renal biopsy repositories with TMAs.Comment: 12 pages, 3 figure