Carotid artery segmentation and plaque quantification in CTA

A novel, slice-based, semi-automatic method for plaque segmentation and quantification in CTA of carotid arteries is introduced. The method starts with semi-automatic, levelset based, lumen segmentation initialized with three points. Pixel based GentleBoost classification is used to segment the inner and outer vessel wall region using distance from the lumen, intensity and Gaussian derivatives as features. 3D calcified regions located within the vessel wall are segmented using a similar set of features and the same classification method. Subsequently, an ellipse-shaped deformable model is fitted using the inner-outer vessel wall and calcium classification, and plaque components within the wall are characterized using HU ranges. The method is quantitatively evaluated on 5 carotid arteries. Vessel and plaque segmentation are compared to the interobserver variability. Furthermore, correlation of slice-based plaque component quantification with the ground truth values is determined. The accuracy of our method is comparable to the interobserver variability.