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.

数字病理学全切片图像 (WSI) 的定量计算分析已显示出不断增加 过去十年精准医疗应用的前景。近年来，这一进展已扩展到肾 病理学，同时看到对来自大型数字 WSI 的深层特征进行客观量化的需求不断增长 肾组织。目前肾活检常规明场目视评估标准无法得出 并量化机器视觉技术引发的大型 WSI 的深层特征。现有计算 肾脏病理工具主要侧重于肾脏微区室的提取和计算分类 肾脏疾病。然而，了解肾微室深层特征之间的相关性 临床生物测定学以及与分子水平数据的相关性仍然是研究和分析的机会 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不是 采用病理学最终用户可以轻松实施的格式。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，并使研究呈指数级增长 通过联盟（包括 肾脏精准医学项目、肾病综合征研究网络、治愈肾小球肾病以及 人类生物分子图谱计划。为了解决上述差距，计算成像专家（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（韩博士） 合作开发了一款基于网络云的最终用户软件，供肾病理学家、肾病学家和 基础科学家。所提出的工具是团队成员之间持续协作的结果。这 拟议的软件将为肾脏病理学最终用户提供以下功能：（i）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/组织形态学 易于使用的基于网络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，允许用户分割多尺度肾 大量肾组织 WSI 的结构，以及 (iv) 细化肾微室的插件 人类与人工智能系统在云端协作的人类人工智能循环设置中的分割 迭代地完善分割模型；最后，（v）深度图像特征的测量 分割肾脏结构，以便对一系列肾脏疾病进行诊断和预后研究。 分布式工具将促进使用联邦学习的多中心研究，而单个中心不会 需要在其机构之外导出包含受保护医疗保健信息的数据，同时仍然参加培训 所提出的系统旨在改进对自己机构数据的细分。最后，系统将提供最终 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，以允许 用户可以在网络云设置中导航图像和分子数据，以实现新的科学发现。