Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology

• URL: http://arxiv.org/abs/2308.05782v1
• Date: Thu, 10 Aug 2023 16:26:03 GMT
• Title: Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology
• Authors: Franklin Hu, Ruining Deng, Shunxing Bao, Haichun Yang, Yuankai Huo
• Abstract summary: We present Omni-Seg, a novel single dynamic network method that capitalizes on multi-site, multi-scale training data. We train a singular deep network using images from two datasets, HuBMAP and NEPTUNE. Our proposed method provides renal pathologists with a powerful computational tool for the quantitative analysis of renal microvascular structures.
• Score: 4.743463035587953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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