Image Mining for Comparative Analysis of Expression Patterns in Tissue Microarray

用于组织微阵列表达模式比较分析的图像挖掘

• 批准号: 7925625
• 负责人: David J Foran
• 金额: $ 56.89万
• 依托单位: UNIV OF MED/DENT NJ-R W JOHNSON MED SCH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7925625
• 关键词: Advanced Development; Algorithms; Architecture; Archives; Cancer Institute of New Jersey; Characteristics; Client; Clinical; Clinical Research; Common Data Element; Communities; Computer Assisted; Computer software; Computers; Data; Data Set; Decision Support Systems; Dentistry; Descriptor; Documentation; Education; Engineering; Ensure; Environment; Evaluation; Exhibits; Funding; Goals; Gold; Human; Image; Image Analysis; Image retrieval system; Laboratories; Lead; Literature; Logic; Malignant Neoplasms; Measurable; Medicine; Metadata; Microscope; Microscopy; Mining; Molecular Profiling; Network-based; New Jersey; Ohio; Online Systems; Pattern; Pattern Recognition; Pennsylvania; Performance; Reproducibility; Research; Research Infrastructure; Research Personnel; Resources; Retrieval; Robotics; Services; Site; Software Tools; Support System; System; Technology; Testing; Texture; Tissue Microarray; Tissues; Universities; Validation; Visual; Vocabulary; Work; base; caGrid; cancer Biomedical Informatics Grid; comparative; computerized tools; computing resources; data modeling; design; image archival system; impression; improved; in vivo; interoperability; medical schools; middleware; novel diagnostics; performance tests; prognostic; programs; tool; tumor; virtual; web-enabled

DESCRIPTION (provided by applicant): Much of the difficulty in rendering consistent evaluation of expression patterns in cancer tissue microarrays arises from subjective impressions of observers. The literature shows that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel diagnostic and prognostic clues which are not apparent by human visual inspection alone. The central objective of this proposal is to design, develop, deploy and evaluate a content-based image retrieval system for performing quick, reliable comparative analysis of expression patterns in cancer tissue microarrays. The proposed project is a natural extension of the lead investigators' prior work and leverages several key resources and computational tools including a web-based image guided decision support system, a distributed telemicroscopy system, a virtual microscopy system, DataCutter which allows distributed execution of algorithms on computer and storage clusters, an intelligent image archival system, and Grid middleware components from the cancer Biomedical Informatics Grid (caBIGTM) In-vivo Imaging Workspace. The distinguishing characteristics of the proposed project are the capacity to support queries and perform comparisons across large datasets originating from both standard robotic and virtual microscopes and the capacity to automatically locate and retrieve those imaged tissue discs from within distributed, "gold-standard" archives which exhibit expression patterns which are most similar to those of a given query disc. Based upon the majority logic of the ranked retrievals query discs will be objectively and reproducibly assessed and classified. To test these technologies a multi-institutional, Grid-enabled laboratory will be established among strategic sites located at The Cancer Institute of New Jersey (CINJ), Columbia University (CU), the Ohio State University (OSU), Rutgers University (RU), the University of Medicine & Dentistry of New Jersey (UMDNJ), and the University of Pennsylvania School of Medicine (UPenn). This laboratory will be built using the caBIG caGrid infrastructure. Upon completion of the project, the software and underlying technologies will be made available to the scientific community as caBIG compliant resources for collaborative research, education and clinical decision support.

描述（由申请人提供）： 在癌症组织微阵列中呈现一致的表达模式的评价的大部分困难来自观察者的主观印象。文献表明，当表征是基于计算机辅助分析，客观性，再现性和灵敏度大大提高。先进的成像和计算工具可能使研究人员能够检测和跟踪可测量参数的细微变化，从而发现新的诊断和预后线索，这些线索仅凭人类视觉检查是不明显的。本提案的中心目标是设计、开发、部署和评估一个基于内容的图像检索系统，用于对癌症组织微阵列中的表达模式进行快速、可靠的比较分析。拟议的项目是首席研究员先前工作的自然延伸，并利用了几个关键资源和计算工具，包括基于网络的图像引导决策支持系统，分布式远程显微镜系统，虚拟显微镜系统，DataCutter（允许在计算机和存储集群上分布式执行算法），智能图像存档系统，和来自癌症生物医学信息网格（caBIGTM）体内成像工作区的网格中间件组件。拟议项目的显着特点是能够支持查询和执行来自标准机器人和虚拟显微镜的大型数据集之间的比较，并能够自动定位和检索那些成像的组织光盘从分布式的，“金标准”的档案表现出的表达模式是最相似的那些给定的查询光盘。基于排序检索的多数逻辑，将客观地且可再现地评估和分类查询盘。为了测试这些技术，将在位于新泽西癌症研究所（CINJ）、哥伦比亚大学（CU）、俄亥俄州州立大学（OSU）、罗格斯大学（RU）、新泽西医学与牙科大学（UMDNJ）和宾夕法尼亚大学医学院（UPenn）的战略地点之间建立一个多机构的网格实验室。该实验室将使用caBIG caGrid基础设施建设。项目完成后，软件和基础技术将作为符合caBIG标准的资源提供给科学界，用于合作研究、教育和临床决策支持。