Shape signatures are fundamental in medicine to characterize abnormal patterns and track their evolution over time. Beyond the diagnosis objective, they are critical to understand the relations between structures and functions as well as to design advanced image-guided therapy. One of the most critical steps in medical imaging remains segmentation, aimed at a fast, precise, robust and reproducible extraction of the organ boundaries.

We present a 3D semi-automatic technique for segmenting the inner boundary of Abdominal Aortic Aneurysms (AAA). The method that is proposed makes use of recent developments from graph theory, the graph-cut algorithms. It requires a limited interaction with the user (for learning the object and background statistical features). Its performance is highlighted on multi-detector CT angiography data. Some perspectives are then sketched in order to show how this result could be improved to feed fluid-structure simulations.