Segmentation of abdominal aortic aneurysms in CT images using a radial model approach

  • Authors:

  • Iván Macía;Jon Haitz Legarreta;Céline Paloc;Manuel Graña;Josu Maiora;Guillermo García;Mariano De Blas

  • Affiliations:

  • Biomedical Applications Department, Vicomtech, Donostia-San Sebastián, Spain;Biomedical Applications Department, Vicomtech, Donostia-San Sebastián, Spain;Biomedical Applications Department, Vicomtech, Donostia-San Sebastián, Spain;Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, Donostia-San Sebastián, Spain;Electronics and Telecommunications Department, Technical University School, University of the Basque Country, Donostia-San Sebastián, Spain;Engineering Systems and Automation Engineering Department, Technical University School, University of the Basque Country, Donostia-San Sebastián, Spain;Interventional Radiology Service, Donostia Hospital, Donostia-San Sebastián, Spain

  • Venue:
  • IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
  • Year:
  • 2009

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Abstract

Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency.