Vessels-cut: A graph based approach to patient-specific carotid arteries modeling

We present a nearly automatic graph-based segmentation method for patient specific modeling of the aortic arch and carotid arteries from CTA scans for interventional radiology simulation. The method starts with morphological-based segmentation of the aorta and the construction of a prior intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weights function that adaptively couples the voxel intensity, the intensity prior, and geometric vesselness shape prior. Finally, the same graph-cut optimization framework is used for nearly automatic removal of a few vessel segments and to fill minor vessel discontinuities due to highly significant imaging artifacts. Our method accurately segments the aortic arch, the left and right subclavian arteries, and the common, internal, and external carotids and their secondary vessels. It does not require any user initialization, parameters adjustments, and is relatively fast (150-470 secs). Comparative experimental results on 30 carotid arteries from 15 CTAs from two medical centres manually segmented by expert radiologist yield a mean symmetric surface distance of 0.79mm (std=0.25mm). The nearly automatic refinement requires about 10 seed points and took less than 2mins of treating physician interaction with no technical support for each case.