A Cloud Based Distributed Tool for Computational Renal Pathology

基于云的分布式计算肾脏病理学工具

• 批准号: 10594498
• 负责人: Pinaki Sarder
• 金额: $ 20.21万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594498
• 关键词: Address; Arteries; Artificial Intelligence; Atlases; Atrophic; Basic Science; Biometry; Chronic; Classification; Clinical; Collaborations; Collection; Computer Analysis; Computer software; Creatinine; Data; Data Set; Development; Diabetic Nephropathy; Diagnostic; Fibrosis; Growth; Health protection; Healthcare; Histology; Human; Image; Image Analysis; Individual; Informatics; Institution; Internet; Investigation; Kidney; Kidney Diseases; Label; Measurement; Metadata; Microanatomy; Modeling; Molecular; Multicenter Studies; Nature; Nephrology; Nephrotic Syndrome; Online Systems; Pathologist; Pathology; Performance; Play; Plug-in; Renal Tissue; Research; Research Personnel; Science; Scientist; Serum; Structure; System; Techniques; Testing; Training; Tubular formation; Vertebral column; Visual Fields; Visualization; arteriole; cloud based; cloud storage; computerized tools; digital; digital pathology; empowerment; federated learning; imaging informatics; improved; interstitial; kidney biopsy; machine vision; member; novel; pathology imaging; precision medicine; prognostic; research study; spatial integration; tool; transcriptomics; whole slide imaging

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.

数字病理的定量计算分析全幻灯片图像（WSIS）已显示出增加 在过去十年中对精确医学应用的承诺。近年来，这一进展已扩展到肾脏 病理学，同时看到对大型数字WSI的深度特征的客观量化的需求越来越大 肾组织。当前的常规Brightfield视觉评估肾脏活检的视觉评估无法引起 并量化机器视觉技术引起的大型WSI的深度特征。现有计算 肾脏病理工具主要集中于提取肾脏微型校区和计算分类 肾脏疾病。但是，了解肾脏微型分配的深度特征之间的相关性 以及临床生物识别技术以及与分子水平数据的相关性仍然是研究的机会和 发现。需要封闭的一个主要差距是计算研究人员开发的工具不是 以病理最终用户可以轻松实现的格式。插件工具的可用性将 增强从事肾脏研究的肾脏病理学家和生物学家，并在研究中提供指数增长 通过通过财团进行的各种肾脏疾病中越来越可用的数字数据集进行的研究 肾脏精密医学项目，肾病综合征研究网络，治疗肾小球肾病和 人类生物分子地图集项目。为了解决上述差距，计算成像的专家（Sarder博士）， 软件科学（Manthey先生），肾病学/基础科学（Rosenberg博士）和肾脏病（Han博士） 已合作为肾病学家，肾脏科医生和 基础科学家。所提出的工具来自团队成员之间的持续合作。这 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WSI和相关元数据的可视化； （ii）微解剖/组织形态学 在易于使用的基于Web的可视化系统中的注释能力，使用户可以协作 进行注释； （iii）自动插件插件，该插件将允许用户细分多尺度肾脏 大量肾脏组织WSI的结构和（iv）插件以完善肾脏微型分室 人工 - 人工智能环境中的分割，人类和AI系统在云中进行了合作 迭代地完善分割模型；最后，（v）测量深度图像特征 分段的肾脏结构可以为肾脏疾病范围诊断和预后研究。 分布式工具将使用联邦学习促进多中心研究 需要在其机构之外使用受保护的医疗保健信息导出数据，同时仍在参加培训 提议的系统，以改善自己的机构数据的细分。最后，该系统将提供最终 用户有能力将空间转录组学分子数据与同一系统中的图像数据集成在一起以允许 用户在Web-Cloud设置中浏览图像以及分子数据，以进行新的科学发现。