Region based level set segmentation of the outer wall of the carotid bifurcation in CTA
This paper presents a level set based method for segmenting the outer vessel wall and plaque components of the carotid artery in CTA. The method employs a GentleBoost classification framework that classifies pixels as calcified region or not, and inside or outside the vessel wall. The combined result of both classifications is used to construct a speed function for level set based segmentation of the outer vessel wall; the segmented lumen is used to initialize the level set. The method has been optimized on 20 datasets and evaluated on 80 datasets for which manually annotated data was available as reference. The average Dice similarity of the outer vessel wall segmentation was 92%, which compares favorably to previous methods.