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: 7691690
• 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 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7691690
• 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; Imaging Device; 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）：