1 / 3

A Virtual Research Environment for Cancer Imaging (VRE-CI)

A Virtual Research Environment for Cancer Imaging (VRE-CI). JISC VRE frameworks phase 3. 18 months 01/05/2009 – 31/10/2010. Project Partner: Microsoft Research Lee Dirks Alex Wade Roger Barga Team members: PI. Prof. Anne E. Trefethen Co-I. Dr. Vicente Grau

tarmon
Download Presentation

A Virtual Research Environment for Cancer Imaging (VRE-CI)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A Virtual Research Environment for Cancer Imaging (VRE-CI) • JISC VRE frameworks phase 3. • 18 months 01/05/2009 – 31/10/2010. • Project Partner: Microsoft Research • Lee Dirks • Alex Wade • Roger Barga • Team members: • PI. Prof. Anne E. Trefethen • Co-I. Dr. Vicente Grau • Project Manager Dr. M. Susana Avila-Garcia. • Technical developer: Dr. Pin Hu. • http://www.oerc.ox.ac.uk/research/vre-ci VRE-CIproject is funded by the Joint Information Systems Committee (JISC) to provide a framework to allow researchers and clinicians involved in Cancer Imaging to share information, images and algorithms.

  2. VRE-CI Extend the functionality of the Research Information Centre (RIC) and include tools for articulating and sharing imaging algorithms through the integration of Trident.

  3. Current activities • Initial experiences: • Installation of the RIC. • Licensing • Becoming familiar with the RIC architecture. • Defining data models: • Using and adapting both DICOM information model and the data model defined by the Cancer Biomedical Informatics Grid (caBIG) of the National Cancer Institute. https://cabig.nci.nih.gov/ • Ensure automatic population of image metadata when loading DICOM files. • Investigating into analysis models (algorithms, inputs & results) • Developing Web parts and Web services for algorithms for cancer image segmentation. • Working with the technical administrator and developer of the Gray Institute to address the access to their server and data.

More Related