Abdominal CTA image analisys through active learning and decision random forests: Aplication to AAA segmentation

Abstract

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 developed 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 using 3D Contrast Computerized Tomography Angiography (CTA) imaging procedures. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm in the CT volume, which is a very time-consuming task. Here we provide results of a novel active learning 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 classifiers.