Pathology Image Informatics Platform for visualization, analysis and management

用于可视化、分析和管理的病理图像信息学平台

• 批准号: 9341177
• 负责人: Metin Nafi Gurcan
• 金额: $ 57.98万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-17 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9341177
• 关键词: Address; Adoption; Advanced Development; Algorithmic Analysis; Algorithms; American; Archives; Big Data; Biological Markers; Cancer Biology; Cancer Prognosis; Clinical; Clinical Pathology; Clinical Research; Clinical Trials; Collaborations; Color; Communities; Community Clinical Oncology Program; Community Trial; Complex; Computational Biology; Computer Vision Systems; Computer software; Country; Data; Data Aggregation; Data Collection; Data Set; Data Storage and Retrieval; Decision Support Systems; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Ensure; Evaluation; Felis catus; Fostering; Foundations; Genetic; Goals; Grant; Health; Histopathology; Human; Image; Image Analysis; Imagery; Informatics; Institution; International; Language; Length; Letters; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Medical Imaging; Medical Students; Molecular Diagnostic Testing; Morphology; Nuclear; Ontology; Optics; Pathologist; Pathology; Pharmacologic Substance; Professional Organizations; Protocols documentation; Pythons; Radiology Specialty; Research; Resolution; Scientist; Site; Slide; Societies; Source; Standardization; Stream; Training; Training and Education; Validation; analytical tool; annotation system; anticancer research; base; biomarker discovery; biomedical scientist; cancer diagnosis; cancer imaging; cancer risk; clinical research site; companion diagnostics; computer human interaction; data exchange; data integration; data sharing; design; digital; digital imaging; drug discovery; high throughput analysis; image archival system; image registration; imaging informatics; improved; in vivo imaging; innovation; insight; interest; malignant breast neoplasm; oncology; open source; photonics; precision medicine; programs; public health relevance; quantitative imaging; radiological imaging; repository; research clinical testing; success; support tools; symposium; tool; tumor; user friendly software; validation studies

 DESCRIPTION (provided by applicant): With the advent of whole slide digital scanners, histopathology slides can be digitized into very high-resolution digital images, realizing a new "big data" stream that can potentially rival "omics data" in size and complexity. Just as with the analysis of high-throughput genetic and expression data, the application of sophisticated image analytic tools and data pipelines can render the often passive data of digital pathology (DP) archives into a powerful source for: (a) rich quantitative insights into cancer biology and (b) companion diagnostic decision support tools for precision medicine. Digital pathology enabled companion diagnostic tests could yield predictions of cancer risk and aggressiveness in a manner similar to molecular diagnostic tests. However, prior to widespread clinical adoption of DP, extensive evaluation of clinical interpretation of DP imaging (DPI) and accompanying decision support tools needs to be undertaken. Wider acceptance of DPI by the cancer community (clinical and research) is hampered by lack of a publicly available, open access image informatics platform for easily viewing, managing, and quantitatively analyzing DPIs. While some commercial platforms exist for viewing and analyzing DPI data, none of these platforms are freely available. Open source image viewing/management platforms that cater to the radiology (e.g. XNAT) and computational biology communities are typically not conducive to handling very large file sizes as encountered with DPI datasets. This multi-PI U24 proposal seeks to expand on an existing, freely available pathology image viewer (Sedeen Image Viewer) to create a pathology informatics platform (PIIP) for managing, annotating, sharing, and quantitatively analyzing DPI data. Sedeen was designed as a universal platform for DPI (by addressing several proprietary scanner formats and "big data" challenges), to provide (1) reliable and useful image annotation tools, and (2) for image registration and analysis of DPI data. Additionally, Sedeen has become an application for cropping large DPIs so that they can be input into programs such as Matlab or ImageJ. Sedeen has been freely available to the public for three years, with over 160 unique users from over 20 countries. Building on the initial successes of Sedeen and its existing user base, our intent is to massively increase dissemination of DPI and algorithms in the cancer research community and clinical trial efforts, as well as to contribute towards the adoption of a rational and standardized set of DP operational conventions. This unique project will allow end users with different needs and technical backgrounds to seamlessly (a) archive and manage, (b) share, and (c) visualize their DPI data, acquired from different sites, formats, and platforms. The PIIP will provide a unified user interface for third party algorithms (nuclear segmentation, color normalization, biomarker quantification, radiology-pathology fusion) and will allow for algorithmic evaluation upon data arising from a plurality of source sites. By partnering with professional societies, we envision that the PIIP user base will expand to include the oncology, pathology, radiology, and pharmaceutical communities.

 描述（由申请人提供）：随着全载玻片数字扫描仪的出现，组织病理学载玻片可以数字化为非常高分辨率的数字图像，实现新的“大数据”流，其在大小和复杂性上可能与“组学数据”相媲美。正如高通量遗传和表达数据的分析一样，复杂的图像分析工具和数据管道的应用可以将数字病理学（DP）档案中通常被动的数据转化为强大的来源：（a）对癌症生物学的丰富定量见解和（B）用于精准医学的伴随诊断决策支持工具。数字病理学支持的伴随诊断测试可以以类似于分子诊断测试的方式预测癌症风险和侵袭性。然而，在DP的广泛临床应用之前，需要对DP成像（DPI）的临床解释和伴随的决策支持工具进行广泛的评价。癌症社区（临床和研究）对DPI的更广泛接受受到缺乏公开可用的开放访问图像信息学平台的阻碍，该平台用于轻松查看，管理和定量分析DPI。虽然存在一些用于查看和分析DPI数据的商业平台，但这些平台都不是免费的。迎合放射学（例如XNAT）和计算生物学社区的开源图像查看/管理平台通常不利于处理DPI数据集遇到的非常大的文件大小。 该多PI U24提案旨在扩展现有的免费病理学图像查看器（SearchImage Viewer），以创建用于管理、注释、共享和定量分析DPI数据的病理学信息学平台（PIIP）。Seedom被设计为DPI的通用平台（通过解决几种专有扫描仪格式和“大数据”挑战），提供（1）可靠和有用的图像注释工具，以及（2）用于DPI数据的图像配准和分析。此外，Seaport已成为一个应用程序，用于裁剪大型DPI，以便将其输入到Matlab或ImageJ等程序中。Sealing已经免费向公众开放了三年，拥有来自20多个国家的160多个独立用户。 在最初的 基于Sealing及其现有用户群的成功，我们的目的是在癌症研究社区和临床试验工作中大规模增加DPI和算法的传播，并为采用一套合理和标准化的DP操作惯例做出贡献。这一独特的项目将使具有不同需求和技术背景的最终用户能够无缝地（a）存档和管理，（B）共享，以及（c）可视化从不同网站、格式和平台获得的DPI数据。PIIP将为第三方算法（核分割、颜色归一化、生物标志物定量、放射学-病理学融合）提供统一的用户界面，并将允许对来自多个源站点的数据进行算法评价。通过与专业协会合作，我们设想PIIP的用户群将扩大到包括肿瘤学，病理学，放射学和制药界。