Poster + Paper
3 April 2024 Multi-scale multi-site renal microvascular structures segmentation for whole slide imaging in renal pathology
Author Affiliations +
Conference Poster
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Franklin Hu, Ruining Deng, Shunxing Bao, Haichun Yang, and Yuankai Huo "Multi-scale multi-site renal microvascular structures segmentation for whole slide imaging in renal pathology", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 1293319 (3 April 2024); https://doi.org/10.1117/12.3006262
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KEYWORDS
Image segmentation

Education and training

Kidney

Head

Neptune

Data modeling

Pathology

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