Title: Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images Show Chinese title

Title: 肾脏微区室分割及其相关病理组学的全幻灯片图像交互式可视化

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). Show Chinese abstract

Abstract: 机器学习技术的应用使得在组织学全切片图像（WSIs）中分割功能组织单元成为可能。 我们构建了一个流程，以应用之前验证的肾脏结构分割模型，并从这些结构中提取定量特征。 这种定量分析还需要对结果进行定性检查，以进行质量控制、探索和交流。 我们将Vitessce基于网络的可视化工具进行了扩展，以实现对多种功能组织单元的分割进行可视化，例如肾脏中的肾小球、小管、动脉/小动脉。 此外，我们提出了一种用于包含多个分割位图文件的标准表示形式，我们以多态方式定义它，以便现有格式包括OME-TIFF、OME-NGFF、AnnData、MuData和SpatialData可以被使用。 我们证明了这些方法使研究人员和更广泛的公众能够交互式地探索包含多个分段实体和相关特征的数据集，包括对来自肾脏精准医学项目（KPMP）和人类生物分子图谱计划（HuBMAP）的活检肾形态测量的探索。