Aortic aneurysms are the 13 th 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.

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