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

TL;DR

This study develops a machine learning model combining U-Net and transformer architectures to accurately segment key kidney tissue compartments in whole slide images, aiding diagnosis of renal TMAs.

Contribution

It introduces a novel segmentation approach that accurately identifies arteries, arterioles, and glomeruli in diverse and severely altered renal biopsy images, even across different staining domains.

Findings

High segmentation accuracy on unseen data

Effective across various renal pathologies

Robust to staining variations

Abstract

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.