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.

采用机器学习和计算机视觉方法，我们通过训练一个分割模型，成功地对肾脏穿刺活检中的决定性诊断性组织区块进行分割，为肾脏血管病的大规模区块特定机器学习和计算机视觉分析奠定了基础。

肾脏血栓性微血管病变（TMA）全区切片图像的诊断组织区块分割