A Cloud Based Distributed Tool for Computational Renal Pathology

数字病理学全切片图像（WSIs）的定量计算分析显示， 在过去的十年里，精准医疗的应用前景广阔。近年来，这一进展已扩展到肾脏 病理学，同时看到越来越需要从大型数字WSI中客观量化深层特征， 肾组织肾活检常规明视野视觉评估的当前标准无法引出 并量化由机器视觉技术引起的大型WSI的深度特征。现有计算 肾脏病理学工具主要集中于肾脏微区室的提取和计算分类 肾脏疾病。然而，了解肾脏微室的深层特征之间的相关性 和临床生物统计学和与分子水平数据的相关性仍然是研究的机会， 的发现需要弥补的一个主要差距是，计算研究人员开发的工具并不 以病理学最终用户可以容易地实现的格式。即插即用工具的可用性将 增强从事肾脏研究的肾脏病理学家和生物学家的能力，并提供研究的指数增长 研究使用越来越多的数字数据集，通过联盟，包括 肾脏精准医学项目，肾病综合征研究网络，治愈肾小球肾病， 人类生物分子图谱计划。为了解决上述差距，来自计算成像（Sarder博士）， 软件科学（Manthey先生）、肾脏病理学/基础科学（Rosenberg博士）和肾脏病学（Han博士） 已经联手开发了一个基于网络云的最终用户软件，用于肾脏病理学家，肾脏病学家， 基础科学家拟议的工具来自团队成员之间的持续合作。的 拟议的软件将为肾脏病理学最终用户提供以下功能：㈠云存储和 数字肾脏病理学WSI和相关元数据的可视化;（ii）显微解剖学/组织形态学 在一个易于使用的基于Web的可视化系统中的注释功能，允许用户在 进行注释;（iii）自动化即插即用插件，允许用户分割多尺度肾脏 用于大批量肾组织WSI的结构，以及（iv）用于细化肾微室间结构的插件。 在人工智能循环设置中进行分割，其中人类和AI系统在云中进行协作 迭代地细化分割模型;以及最后，（v）测量 分割的肾脏结构，使诊断和预后研究的肾脏疾病谱。 分布式工具将促进使用联合学习的多中心研究，而单个中心将无法 需要在其研究所之外导出受保护的医疗保健信息的数据，同时仍参与培训 拟议的系统，以改善自己的机构数据的分割。最后，该系统将提供端- 用户能够将空间转录组学分子数据与图像数据集成在同一系统中， 用户可以在网络云设置中导航图像和分子数据，以进行新的科学发现。

Quantitative computational analysis of digital pathology whole slide images (WSIs) has shown increasing promise for precision medicine applications in last decade. In recent years, this progress has extended to renal pathology, while seeing a growing need of objective quantification of deep features from large digital WSIs of renal tissues. The current standard of routine brightfield visual assessment of renal biopsies is unable to elicit and quantify the deep features from large WSIs elicited by machine vision techniques. Existing computational renal pathology tools primarily focus on extraction of renal micro-compartments and computational classification of renal diseases. However, understanding the correlation between deep features of renal micro-compartments and clinical biometrics and correlations with molecular level data remain as opportunities for investigation and discovery. A major gap that needs to be closed is that the tools developed by computational researchers are not in a format that can be easily implemented by pathology end-users. The availability of plug-and-play tools will empower renal pathologists and biologists engaged in kidney research, and offer exponential growth in research studies using increasingly available digital datasets across various kidney diseases via consortia including the Kidney Precision Medicine Project, Nephrotic Syndrome Study Network, Cure Glomerulonephropathy, and Human Biomolecular Atlas Project. To address the above gap, experts from computational imaging (Dr. Sarder), software science (Mr. Manthey), nephropathology/basic science (Dr. Rosenberg), and nephrology (Dr. Han) have teamed up to develop a web-cloud based end-user software for nephropathologists, nephrologists, and basic scientists. The proposed tool emerges from ongoing collaboration between the team members. The proposed software will offer the following functionalities to renal pathology end-users: (i) cloud storage and visualization of digital renal pathology WSIs and associated metadata; (ii) microanatomic/histomorphologic annotation capability in an easy-to-use web-based visualization system, allowing users to collaborate while conducting annotation; (iii) automated plug-and-play plugins that would allow users to segment multi-scale renal structures for a large batch of renal tissue WSIs, and (iv) plugins to refine renal micro-compartmental segmentation in a human-artificial-intelligence-loop set-up where humans and AI system collaborate in the cloud to refine the segmentation models iteratively; and finally, (v) measurement of deep image features on the segmented renal structures to enable diagnostic and prognostic research for the spectrum of renal diseases. The distributed tool will facilitate multi-center studies using federated learning where individual centers will not need to export data with protected healthcare information outside their institutes, while still participating in training the proposed system to improve segmentation on their own institutional data. Finally, the system will offer end- users the ability to integrate spatial transcriptomics molecular data with image data in the same system to allow users to navigate the images as well as molecular data in a web-cloud set-up for new scientific discovery.