A Cloud Based Distributed Tool for Computational Renal Pathology

数字病理全片图像(WSIS)的定量计算分析显示出越来越多的数据 在过去的十年中，精准医学应用前景看好。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信息社会信息系统中客观量化深层特征 肾组织。目前肾活检的常规Brightfield目测评估标准不能引出 并量化机器视觉技术从大型信息社会世界信息系统中提取的深层特征。现有计算 肾脏病理学工具主要侧重于肾脏微区的提取和计算分类 肾脏疾病。然而，了解肾脏微区深部特征之间的相关性 临床生物测定和与分子水平数据的相关性仍然是研究和 发现号。需要弥合的一个主要差距是，计算研究人员开发的工具并不是 以一种病理终端用户可以轻松实现的格式。即插即用工具的可用性将 为从事肾脏研究的肾脏病理学家和生物学家提供支持，并在研究方面提供指数式增长 通过财团使用日益可用的数字数据集进行的各种肾脏疾病的研究，包括 肾精准医学项目、肾病综合征研究网络、治疗肾小球疾病 人类生物分子图谱计划。为了解决上述差距，来自计算成像的专家(萨德博士)， 软件科学(曼泰先生)、肾脏病理学/基础科学(罗森博格博士)和肾脏病(韩博士) 已联手开发一款基于Web云的最终用户软件，供肾病病理学家、肾病学家和 基础科学家。拟议的工具来自团队成员之间的持续合作。这个 建议的软件将为肾脏病理最终用户提供以下功能：(I)云存储和 数字肾脏病理WSIS及其关联元数据的可视化；(Ii)显微解剖/组织形态 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)自动即插即用插件，允许用户分割多个比例的肾脏 用于大批量肾组织WSIS的结构，以及(Iv)用于改进肾脏微隔室的插件 人类和人工智能系统在云中协作的人工-人工智能-环路设置中的分段 迭代地改进分割模型；最后，(V)在 分段的肾脏结构，以实现对肾脏疾病谱的诊断和预后研究。 分布式工具将使用联合学习促进多中心研究，而各个中心不会 需要在机构外导出包含受保护医疗保健信息的数据，同时仍在参加培训 建议系统完善对自身机构数据的分割。最后，该系统将提供终端- 用户能够在同一系统中集成空间转录分子数据和图像数据，以允许 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.