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).
@article{arxiv.2510.19499,
title = {Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images},
author = {Mark S. Keller and Nicholas Lucarelli and Yijiang Chen and Samuel Border and Andrew Janowczyk and Jonathan Himmelfarb and Matthias Kretzler and Jeffrey Hodgin and Laura Barisoni and Dawit Demeke and Leal Herlitz and Gilbert Moeckel and Avi Z. Rosenberg and Yanli Ding and Pinaki Sarder and Nils Gehlenborg},
journal= {arXiv preprint arXiv:2510.19499},
year = {2025}
}