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.

抽象的 现代医学已进入大数据革命的早期阶段；显微技术、数字化技术取得巨大进展； 除了放射学等成熟技术之外，电子学、光子学和测序技术也已 能够从人类受试者中生成大量多模式、多尺度、多组学数据。 结果数据的数字化，加上计算机硬件和软件的先进水平，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的发现工作很难通过手动方式进行。两个重要的方法。 沿着这个方向由美国国立卫生研究院精心策划，即人类生物分子图谱； 计划 (HuBMAP) 和肾脏精准医学项目 (KPMP) 前者侧重于定义参考。 后者跨生物尺度的不同组织的解剖图谱仅专注于定义结构和结构。 稳态肾脏的功能图谱及其疾病状态偏差上述联合体。 致力于生成多尺度、组学数据，首要目标是融合海量多模态数据。 数据开发参考和疾病患者组织异质性程度的综合模型， 以便对组织的临床解释能够更加客观化，在实现多尺度的崇高目标之前， 多模态数据融合，第一步是使用来自单一模态和单一数据的数据进行试点实验 假设成功，该管道可以验证统计参考范围。 然后可以扩展到不同的器官类型和规模，在这个 HubMAP 补充中。 应用中，我们建议研究“参考”肾组织明场的形态结构多样性 我们使用全景卷积神经网络从 HuBMAP、KPMP 和其他来源获取整个幻灯片图像。 将所得的结构分布异质性与从同等慢性肾脏获得的结构分布异质性进行比较 来自KPMP的疾病案例用于基准目的，以精确确定参考上限 PI 是计算肾脏病理学方面的专家，并产生了大量的结构分布。 肾区客观量化、肾脏疾病计算分类的结果，以及 此外，PI 是研究者的一部分。 KPMP 团队，为 KPMP 数据的技术数据分析管道的开发做出了贡献。 在分析 GTEx 肾组织图像数据以及构建基于云的网络浏览器方面拥有丰富的经验 可访问的图像和组学数据存档和可视化系统，带有内置插件，可用于大规模人工智能分析 缩放图像和组学数据，最终用户需要最少的技术知识来操作这项工作。 将为 HuBMAP 提供全面的数字病理学框架，用于结构、效率的图像分析 在结构注释中，“参考”统计组织图谱生成，以及协助病理学家进行大量 体积注释并促进分子整合研究。