Abdominal Aortic Aneurysm (AAA) is a local dilation of the Aorta that occurs between the renal and iliac arteries. The weakening of the aortic wall leads to its deformation and the generation of a thrombus. Recently, the procedure used for treatment involves the insertion of a endovascular prosthetic (EVAR), which has the advantage of being a minimally invasive procedure but also requires monitoring to analyze postoperative patient outcomes. 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 active learning hybrid approach for the semi-automatic detection and segmentation of the lumen and the thrombus of the AAA, which uses image intensity features and discriminative Random Forest classfiers.
