Segmenting microvascular structures, such as arterioles, venules, and capillaries, from human kidney whole slide images (WSI) in renal pathology has garnered significant interest. The current manual segmentation approach is laborious and impractical for large-scale digital pathology images. To address this, deep learning-based methods have emerged for automatic segmentation. However, a gap exists in current deep learning segmentation methods, as they are typically designed and limited by using single-site single-scale data for training. In this paper, we introduce a novel single dynamic network method (Omni-Seg), which harnesses multi-site multi-scale training data, utilizing partially labeled images where only one tissue type is labeled per training image for microvascular structure segmentation. We train a single deep network using images from two datasets, HuBMAP and NEPTUNE, with different scales (40×, 20×, 10×, 5×). Our experimental results demonstrate that our approach achieves higher Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores. This proposed method empowers renal pathologists with a computational tool for quantitatively assessing renal microvascular structures.
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