In the diagnosis of coronary artery disease, 3D-multi-slice computed tomography (MSCT) has recently become more and more important. In this work, an anatomical-based method for the segmentation of atherosclerotic coronary arteries in MSCT is presented. This technique is able to bridge severe stenosis, image artifacts or even full vessel occlusions. Different anatomical structures (aorta, blood-pool of the heart chambers, coronary arteries and their orifices) are detected successively to incorporate anatomical knowledge into the algorithm. The coronary arteries are segmented by a simulated wave propagation method to be able to extract anatomically spatial relations from the result. In order to bridge segmentation breaks caused by stenosis or image artifacts, the spatial location, its anatomical relation and vessel curvature-propagation are taken into account to span a dynamic search space for vessel bridging and gap closing. This allows the prevention of vessel misidentifications and improves segmentation results significantly. The robustness of this method is proven on representative medical data sets.