Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Keller, Mark S. Lucarelli, Nicholas Chen, Yijiang Border, Samuel Janowczyk, Andrew Himmelfarb, Jonathan Kretzler, Matthias Hodgin, Jeffrey Barisoni, Laura Demeke, Dawit Herlitz, Leal Moeckel, Gilbert Rosenberg, Avi Z. Ding, Yanli Sarder, Pinaki Gehlenborg, Nils |
| author_facet | Keller, Mark S. Lucarelli, Nicholas Chen, Yijiang Border, Samuel Janowczyk, Andrew Himmelfarb, Jonathan Kretzler, Matthias Hodgin, Jeffrey Barisoni, Laura Demeke, Dawit Herlitz, Leal Moeckel, Gilbert Rosenberg, Avi Z. Ding, Yanli Sarder, Pinaki Gehlenborg, Nils |
| contents | Application of machine learning techniques enables segmentation of functional tissue units in histology whole-slide images (WSIs). We built a pipeline to apply previously validated segmentation models of kidney structures and extract quantitative features from these structures. Such quantitative analysis also requires qualitative inspection of results for quality control, exploration, and communication. We extend the Vitessce web-based visualization tool to enable visualization of segmentations of multiple types of functional tissue units, such as, glomeruli, tubules, arteries/arterioles in the kidney. Moreover, we propose a standard representation for files containing multiple segmentation bitmasks, which we define polymorphically, such that existing formats including OME-TIFF, OME-NGFF, AnnData, MuData, and SpatialData can be used. We demonstrate that these methods enable researchers and the broader public to interactively explore datasets containing multiple segmented entities and associated features, including for exploration of renal morphometry of biopsies from the Kidney Precision Medicine Project (KPMP) and the Human Biomolecular Atlas Program (HuBMAP). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images Keller, Mark S. Lucarelli, Nicholas Chen, Yijiang Border, Samuel Janowczyk, Andrew Himmelfarb, Jonathan Kretzler, Matthias Hodgin, Jeffrey Barisoni, Laura Demeke, Dawit Herlitz, Leal Moeckel, Gilbert Rosenberg, Avi Z. Ding, Yanli Sarder, Pinaki Gehlenborg, Nils Quantitative Methods Human-Computer Interaction Application of machine learning techniques enables segmentation of functional tissue units in histology whole-slide images (WSIs). We built a pipeline to apply previously validated segmentation models of kidney structures and extract quantitative features from these structures. Such quantitative analysis also requires qualitative inspection of results for quality control, exploration, and communication. We extend the Vitessce web-based visualization tool to enable visualization of segmentations of multiple types of functional tissue units, such as, glomeruli, tubules, arteries/arterioles in the kidney. Moreover, we propose a standard representation for files containing multiple segmentation bitmasks, which we define polymorphically, such that existing formats including OME-TIFF, OME-NGFF, AnnData, MuData, and SpatialData can be used. We demonstrate that these methods enable researchers and the broader public to interactively explore datasets containing multiple segmented entities and associated features, including for exploration of renal morphometry of biopsies from the Kidney Precision Medicine Project (KPMP) and the Human Biomolecular Atlas Program (HuBMAP). |
| title | Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images |
| topic | Quantitative Methods Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.19499 |