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Imaging Management and Analysis Core

影像管理和分析核心

基本信息

批准号：
10166786
负责人：
JOE W. GRAY
金额：
$ 20.41万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10166786
关键词：
3-Dimensional Address Algorithmic Analysis Algorithms Architecture Back Biological Assay Biological Markers Cell Culture Techniques Cell Line Cells Cellular Morphology Characteristics Classification Collaborations Complex Computer Systems Computer software Custom Data Data Storage and Retrieval Databases Development Electron Microscope Electrons Environment Face Heterogeneity High Performance Computing Image Image Analysis Imaging Device Immunofluorescence Immunologic Individual Infrastructure Ingestion Label Light Microscope Link Machine Learning Measurement Measures Metadata Methods Microscopic Modeling Nature Performance Periodicity Pharmaceutical Preparations Phenotype Population Procedures Process Protocols documentation Qi Quality Control Resolution Resource Sharing Resources Scanning Electron Microscopy Selection Criteria Structure System Systems Biology Tissues Visualization Visualization software base cancer heterogeneity data management experimental study feature extraction flexibility fluorescence imaging high dimensionality image reconstruction imaging Segmentation imaging approach imaging system insight light microscopy multimodality novel open source outreach protocol development quality assurance quantitative imaging repository response screening segmentation algorithm tool

项目摘要

ABSTRACT – Imaging Management and Analysis Core The Imaging Management and Analysis Core (Imaging Core) will meet the requirements of the three Measuring, Modeling, and Controlling Heterogeneity-Cancer Systems Biology Consortium (M2CH-CSBC) Projects and the Outreach Core by developing and integrating state-of-the-art resources for a complete imaging workflow. Specifically, we will 1) use high-content imaging to measure baseline and drug and microenvironment response phenotypes separately and in combination in a panel of cell lines, 2) optimize and deploy novel, highly multiparameter cyclic multiplexed immunofluorescence (cmIF) imaging workflows to measure microenvironment and drug responses in heterogeneous tissues in 2D and 3D, and 3) deploy correlative light/electron microscopic analysis based on super-resolution light microscopy and scanning electron microscopy to provide the ultrastructural cellular context. To execute these tasks in close collaboration with the Projects, the Imaging Core will expand on existing resources and build a customized imaging environment to a) acquire image data in automated ways from the multiple imaging instruments, following precise protocols and maintaining complete metadata, b) store images and metadata and all derived and associated measurement data in highly robust databases and repositories with powerful query, visualization, and programmer access tools, c) quantitatively analyze images by deploying and developing state-of-the-art image reconstruction methods, image segmentation algorithms, feature extraction and classification routines, normalization procedures, and machine learning approaches to classify image features at multiple resolution levels, and d) develop novel visualization methods for interacting with images and extracted features and models in combination with experimental metadata and data from external databases.

摘要：影像管理与分析核心