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 现代医学进入了大数据革命的早期阶段。显微镜，数字的大规模进展 电子，光子学和测序技术除了放射学等建立技术外，还具有 启用了人类受试者大量的多模式，多尺度的多级数据。更远， 所得数据的数字化以及计算机硬件和软件的高级状态的数字化具有 为计算图像科学家打开了新的机会，以识别以前未知的统计数据 来自Big Data的生物标志物通过手动手段可以棘手。两项重要的努力 沿着这个方向由美国国立卫生研究院策划；即人分子地图集 计划（Hubmap）和肾脏精密医学项目（KPMP）。前者专注于定义参考 跨生物量表的解剖图谱，用于多样性组织。后来仅着重于定义结构和 体内肾脏及其疾病状态出发的功能性地图集。上面提到的财团 致力于生成多尺度的-omics数据。主要目的是融合大型多模式 数据以发展参考和疾病患者中组织异质性程度的综合模型， 因此，对组织的临床解释可以更加客观。在实现多尺度的崇高目标之前 多模式数据融合，第一步是使用单个模式和单个模式的数据进行试验实验 器官可以验证可以适当定义统计参考范围。假设成功，这条管道 然后可以缩放到分解器官的类型和秤。朝向这一目标，在这种hubmap补充中 应用，我们建议研究“参考”肾脏组织Brightfield中的形态结构多样性 使用全景卷积神经网络来自Hubmap，KPMP和其他来源的全幻灯片图像。我们 将将所得的结构分布异质性与从等效慢性肾脏获得的结构分布异质性进行比较 毕马威（KPMP）的疾病病例用于基准测试目的 结构分布。 PI是计算肾脏病理学专家，已经产生了许多 有关肾脏室的客观数量，肾脏疾病的计算分类和 肾脏组织图像的临床生物识别技术的计算预测。此外，PI是研究人员的一部分 毕马威会计师事务所的团队，为毕马威（KPMP）数据的技术数据分析管道的开发做出了贡献。 Pi具有 在分析的GTEX肾脏组织图像数据以及构建基于云的Web浏览器方面的重要经验 可访问的图像和OMICS数据档案和可视化系统，并带有内置插件，用于大型AI分析 比例图像和OMICS数据，这需要最小的技术知识才能由最终用户运作。这项工作 将为HUBMAP提供一个综合的数字病理框架，用于结构的图像分析，效率 在结构注释中，“参考”统计组织地图集的产生，并协助病理学家大量 体积注释并促进分子整合研究。