Share Email Print

Proceedings Paper

Semi-automatic aortic aneurysm analysis
Author(s): Osman Bodur; Leo Grady; Arthur Stillman; Randolph Setser; Gareth Funka-Lea; Thomas O'Donnell
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Aortic aneurysms are the 13th leading cause of death in the United States. In standard clinical practice, assessing the progression of disease in the aorta, as well as the risk of aneurysm rupture, is based on measurements of aortic diameter. We propose a method for automatically segmenting the aortic vessel border allowing the calculation of aortic diameters on CTA acquisitions which is accurate and fast, allowing clinicians more time for their evaluations. While segmentation of aortic lumen is straightforward in CTA, segmentation of the outer vessel wall (epithelial layer) in a diseased aorta is difficult; furthermore, no clinical tool currently exists to perform this task. The difficulties are due to the similarities in intensity of surrounding tissue (and thrombus due to lack of contrast agent uptake), as well as the complications from bright calcium deposits. Our overall method makes use of a centerline for the purpose of resampling the image volume into slices orthogonal to the vessel path. This centerline is computed semi-automatically via a distance transform. The difficult task of automatically segmenting the aortic border on the orthogonal slices is performed via a novel variation of the isoperimetric algorithm which incorporates circular constraints (priors). Our method is embodied in a prototype which allows the loading and registration of two datasets simultaneously, facilitating longitudinal comparisons. Both the centerline and border segmentation algorithms were evaluated on four patients, each with two volumes acquired 6 months to 1.5 years apart, for a total of eight datasets. Results showed good agreement with clinicians' findings.

Paper Details

Date Published: 29 March 2007
PDF: 10 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65111G (29 March 2007); doi: 10.1117/12.710719
Show Author Affiliations
Osman Bodur, Siemens Corporate Research (United States)
Leo Grady, Siemens Corporate Research (United States)
Arthur Stillman, Emory Univ. Hospital (United States)
Randolph Setser, Cleveland Clinic Foundation (United States)
Gareth Funka-Lea, Siemens Corporate Research (United States)
Thomas O'Donnell, Siemens Corporate Research (United States)

Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

© SPIE. Terms of Use
Back to Top