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.

翻译：血栓性微血管病（TMAs）在肾活检组织学中表现为一系列急性和慢性病变的广谱特征。目前缺乏用于肾活检诊断TMAs的精确标准。作为迈向基于机器学习和计算机视觉分析肾活检全切片图像的第一步，我们在包含TMAs及其相似疾病（具有类似TMA肾病理学表现的不同疾病，如重度良性肾硬化、多种血管炎、贝伐珠单抗相关性肾小球病、小动脉轻链沉积病）的肾活检全切片图像数据集上，训练了针对决定性的诊断性肾组织区室（动脉、小动脉、肾小球）的分割模型。该分割模型将基于U-Net的组织检测架构与Shifted Windows-Transformer架构相结合，即使对于最严重病变的肾小球、小动脉和动脉也能达到优异的分割结果，甚至适用于来自不同肾病理实验室的未见染色域图像。通过对人类肾血管病变中决定性的肾活检区室进行精确自动分割，我们为基于机器学习和计算机视觉的大规模肾活检库TMAs区室特异性分析奠定了基础。