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Details of Grant 

EPSRC Reference: GR/M52847/01
Title: AUTOMATIC GENERATION OF 3D ANATOMICAL MODELS:TRANING & DECISION MAKING IN KEYHOLE VASCULAR INTERVENTION
Principal Investigator: Berry, Dr E
Other Investigators:
Scott, Professor DJA Boyle, Professor R Kessel, Dr D
Researcher Co-Investigators:
Project Partners:
Department: Medicine
Organisation: University of Leeds
Scheme: Standard Research (Pre-FEC)
Starts: 01 July 1999 Ends: 31 August 2001 Value (£): 145,537
EPSRC Research Topic Classifications:
Image & Vision Computing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
The minimally invasive procedure for endovascular repair of abdominal aortic aneurysms (AAA) is rapidly diffusing from a few specialist centres. Patient assessment for the procedure is a three-step process (also AAA: Access, Anchorage, Adequate). For Access the size, path and composition of the vessels must be suitable; for Anchoring an endovascular device there must be sufficient healthy vessel wall and an Adequate blood supply to the digestive organs must be available after device deployment. If the procedure is feasible (as it may be for only 10% of patients assessed), measurements are made to find the size of the endovascular device required. We shall develop an automated segmentation and measurement tool to support this pre-procedure process. The segmentation results will also be applied to enhance an existing prototype simulator for training and planning. The new segmentation methodology develops our existing 3D deformable model for branching anatomy. The enhancements are:1) Inclusion of knowledge from anatomical date2) Addition of further image-based parameters and,3) A surface optimisation stage to remove errors.Validation is an essential part of the project, and tools will be developed and applied to validate the segmentation results and the tools for patient assessment and stent design.
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Further Information:  
Organisation Website: http://www.leeds.ac.uk