KULMAP: Human Kidney, urinary tract and lung mapping center

KULMAP：人类肾脏、泌尿道和肺部绘图中心

• 批准号: 10413576
• 负责人: James S. Hagood
• 金额: $ 10万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-25 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413576
• 关键词: Anatomy; Atlases; Benchmarking; Big Data; Biological; Biological Markers; Biometry; Chronic Kidney Failure; Classification; Clinical; Computer Hardware; Computer software; Coupled; Data; Derivation procedure; Development; Disease; Electronics; Generations; Genotype-Tissue Expression Project; Goals; Heterogeneity; Human; Human BioMolecular Atlas Program; Image; Image Analysis; Internet; Kidney; Kidney Diseases; Knowledge; Link; Lung; Manuals; Microscopy; Modality; Modeling; Modern Medicine; Molecular; Morphology; Multiomic Data; Organ; Pathologist; Pathology; Pathway interactions; Patients; Phase; Radiology Specialty; Reference Values; Renal Tissue; Research Personnel; Source; Structure; System; Techniques; Technology; Tissue imaging; Tissues; United States National Institutes of Health; Urinary tract; Work; body system; cell type; cloud based; convolutional neural network; data analysis pipeline; data archive; data fusion; data visualization; digital; digital pathology; diverse data; experience; experimental study; human subject; imaging scientist; morphometry; multimodal data; multimodality; photonics; precision medicine; success; whole slide imaging

Abstract Modern medicine has entered the early phase of big data revolution; massive progress in microscopy, digital electronics, photonics, and sequencing technologies, in addition to established techniques like radiology, have enabled generation of multi-modal, multi-scale, multi-omics data in large volume from human subjects. Further, the digitization of the resulting data, coupled with the advanced state of computer hardware and software, has opened up new opportunities for computational image scientists to identify previously unknown statistical biomarkers from big-data whose discovery is otherwise intractable by manual means. Two important efforts along this direction are orchestrated by the National Institutes of Health; namely, the Human BioMolecular Atlas Program (HuBMAP) and Kidney Precision Medicine Project (KPMP). The former focuses on defining a reference anatomical atlas across biological scale for diverse tissues. The latter focuses solely on defining a structural and functional atlas of the homeostatic kidney and their disease state deviations. The consortiums mentioned above are committed to generate multi-scale, -omics data. The primary objective is to fuse the massive multi-modal data to develop a comprehensive model of the extent of tissue heterogeneity in reference and disease patients, so that clinical interpretations of tissue can be more objectified. Before approaching the lofty goal of multi-scale, multi-modal data fusion, the first step is to conduct pilot experiments using data from single modalities and single organs to validate that a statistical reference range can be adequately defined. Presuming success, this pipeline can then be scaled to diverse organ types and scales. Toward that objective, in this HubMAP supplemental application, we propose to investigate morphological structural diversity in ‘reference’ kidney tissue brightfield whole slide images from HuBMAP, KPMP, and other sources using a panoptic convolutional neural network. We will compare the resulting structural distribution heterogeneity with that obtained from equivalent chronic kidney disease cases from KPMP for benchmarking purpose to precisely establish the upper limit on the reference structural distributions. The PI is an expert in computational renal pathology, and has generated numerous results on objective quantification of renal compartments, computational classification of renal diseases, and computational prediction of clinical biometrics from renal tissue images. Further, the PI is part of the investigator team of KPMP, contributing to the development of a technical data analysis pipeline for KPMP data. The PI has significant experience in analyzing GTEx renal tissue image data, as well as building a cloud-based, web browser accessible image and omics data archival and visualization system with built-in plugins for AI analysis on large scale image and omics data, which requires minimal technical knowledge to operate by end-users. This work will provide HuBMAP a comprehensive digital pathology framework for image analysis of structures, efficiency in structural annotation, a ‘reference’ statistical tissue atlas generation, as well as assist pathologists with large volume annotations and facilitate molecular integration studies.

摘要 现代医学进入大数据革命早期；显微镜、数字化取得巨大进步 电子学、光子学和测序技术，除了像放射学这样的成熟技术之外，还有 能够从人类受试者中生成大量的多模式、多尺度、多组学数据。此外， 结果数据的数字化，再加上计算机硬件和软件的先进状态，已经 为计算图像科学家识别以前未知的统计数据开辟了新的机会 来自大数据的生物标记物，这些生物标记物的发现本来很难通过人工手段解决。两个重要的努力 沿着这个方向的是由美国国立卫生研究院编排的，即人类生物分子图谱 项目(HuBMAP)和肾脏精准医学项目(KPMP)。前者侧重于定义引用 跨越生物尺度的不同组织的解剖图谱。后者仅侧重于定义结构和 动态平衡肾脏及其疾病状态偏差的功能图谱。上述财团 致力于生成多尺度的组学数据。主要目标是融合大规模的多模式 数据来开发参考和疾病患者组织异质性程度的综合模型， 从而使临床对组织的解释更加客观化。在接近多尺度的崇高目标之前， 多模式数据融合，第一步是使用单一模式和单一模式的数据进行试点实验 各机构验证统计参考范围是否可以得到充分界定。假设成功，这条管道 然后可以根据不同的器官类型和比例进行调整。为了实现这一目标，在这份HubMAP补充资料中 应用，我们建议研究肾组织的形态结构多样性。 使用全视卷积神经网络从HuBMAP、KPMP和其他来源获得完整的幻灯片图像。我们 将所得到的结构分布异质性与同等慢性肾脏的结果进行比较 疾病病例以KPMP为标杆，以精确设定上限为参考 结构分布。PI是计算肾脏病理学方面的专家，并已产生了许多 肾脏分区的客观量化、肾脏疾病的计算分类以及 从肾组织图像进行临床生物识别的计算预测。此外，私家侦探也是调查员的一部分 KPMP团队，为开发KPMP数据的技术数据分析管道做出贡献。少年派有 具有分析GTEx肾组织图像数据以及构建基于云的Web浏览器的丰富经验 具有内置插件的可访问的图像和组学数据归档和可视化系统，用于对 扩展图像和组学数据，最终用户只需极少的技术知识即可操作。这部作品 将为HuBMAP提供一个全面的数字病理框架，用于图像结构的分析，效率 在结构注释中，一种‘参考’统计组织图谱的生成，以及帮助病理学家进行大量 卷注解和促进分子集成研究。