Multi-scale Multi-site Renal Microvascular Structures Segmentation for
  Whole Slide Imaging in Renal Pathology
Franklin Hu Ruining Deng Shunxing Bao Haichun Yang Yuankai Huo
Segmentation of microvascular structures, such as arterioles, venules, and
capillaries, from human kidney whole slide images (WSI) has become a focal
point in renal pathology. Current manual segmentation techniques are
time-consuming and not feasible for large-scale digital pathology images. While
deep learning-based methods offer a solution for automatic segmentation, most
suffer from a limitation: they are designed for and restricted to training on
single-site, single-scale data. In this paper, we present Omni-Seg, a novel
single dynamic network method that capitalizes on multi-site, multi-scale
training data. Unique to our approach, we utilize partially labeled images,
where only one tissue type is labeled per training image, to segment
microvascular structures. We train a singular deep network using images from
two datasets, HuBMAP and NEPTUNE, across different magnifications (40x, 20x,
10x, and 5x). Experimental results indicate that Omni-Seg outperforms in terms
of both the Dice Similarity Coefficient (DSC) and Intersection over Union
(IoU). Our proposed method provides renal pathologists with a powerful
computational tool for the quantitative analysis of renal microvascular
structures.