Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images

TL;DR

This paper presents an interactive visualization pipeline for kidney tissue segmentation in whole-slide images, integrating multiple segmentation types and features to facilitate exploration, quality control, and communication of renal histology data.

Contribution

It extends the Vitessce tool to visualize multiple kidney tissue units and introduces a polymorphic file format standard for multi-segmentation data representation.

Findings

Enables interactive exploration of kidney tissue segmentations.

Supports datasets from KPMP and HuBMAP.

Facilitates quality control and data communication.

Abstract

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).