Region-based deformable model for aortic wall segmentation

We present a method for automated segmentation of aortic wall from computed tomography angiography (CTA) images, which is the most critical step in quantitative image analysis of abdominal aortic aneurysm (AAA). The two-step method uses geometric deformable models based on the level set algorithm. In the first step, the inner aortic boundary is segmented, followed by segmentation of the outer aortic boundary in the second step. The inner aortic boundary in CTA images have high-contrast edges, which makes this step relatively easy. Segmentation is performed by a 2-D edge-based deformable model. The outer aortic boundary has low contrast edges, resulting in difficult segmentation. To address the problem of ill-defined edges, in our approach we use a 2-D region-based deformable model. We propose several knowledge-based constraints that help in aortic wall segmentation. In our previous research on AAA segmentation, we developed several segmentation methods using edge-based geometric deformable model approaches. In this paper, we present a new region-based segmentation method that uses a geometric deformable model. All specific constraints to the AAA segmentation are incorporated directly into the geometric deformable model, producing a more compact solution. The method has been tested on CTA scans of twelve patients and has shown promising results.