Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images.
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).
Authors
- Laura Barisoni (ORCID: https://orcid.org/0000-0003-0848-9683)
- Avi Z. Rosenberg (ORCID: https://orcid.org/0000-0003-2356-950X)
- Pinaki Sarder (ORCID: https://orcid.org/0000-0003-2450-5233)
- Samuel Border (ORCID: https://orcid.org/0000-0002-8806-6965)
- Jonathan Himmelfarb (ORCID: https://orcid.org/0000-0002-3319-1224)
- Gilbert W. Moeckel (ORCID: https://orcid.org/0000-0002-6059-0837)
- Matthias Kretzler (ORCID: https://orcid.org/0000-0003-4064-0582)
- Andrew Janowczyk (ORCID: https://orcid.org/0000-0003-2982-4321)
- Leal Herlitz (ORCID: https://orcid.org/0000-0003-2665-8559)
- Nicholas Lucarelli (ORCID: https://orcid.org/0000-0002-5454-7074)
- Yanli Ding
- Mark S. Keller (ORCID: https://orcid.org/0000-0003-3003-874X)
- Jeffrey B. Hodgin (ORCID: https://orcid.org/0000-0003-0534-3048)
- Yijiang Chen (ORCID: https://orcid.org/0000-0002-3224-4339)
- Dawit Demeke (ORCID: https://orcid.org/0009-0002-0905-178X)
Institutions
- University of Minnesota (US)
- Georgia Institute of Technology (US)
- Cleveland Clinic (US)
- Harvard University (US)
- Johns Hopkins University (US)
- Emory University (US)
- Duke University (US)
- University of Michigan (US)
- Johns Hopkins Medicine (US)
- University of Florida Health (US)
- Yale University (US)
- Case Western Reserve University (US)
- Stanford University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- PubMed
- Published
- 2025-10-22
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Diabetes and Digestive and Kidney Diseases