Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA Olivier Cuisenaire a , Sunny Virmani b , Mark E. Olszewski b , Roberto Ardon a a Medisys Research Lab, Philips Healthcare, 51 Rue Carnot, 92156 Suresnes, France b CT Clinical Science, Philips Healthcare, 595 Miner Rd, Highland Heights, Ohio 44143, USA ABSTRACT We propose a method for segmenting and labeling the main head and neck vessels (common, internal, external carotid, vertebral) from a contrast enhanced computed tomography angiography (CTA) volume. First, an initial centerline of each vessel is extracted. Next, the vessels are segmented using 3D active objects initialized using the first step. Finally, the true centerline is identified by smoothly deforming it away from the segmented mask edges using a spline-snake. We focus particularly on the novel initial centerline extraction technique. It uses a locally adaptive front propagation algorithm that attempts to find the optimal path connecting the ends of the vessel, typically from the lowest image of the scan to the Circle of Willis in the brain. It uses a patient adapted anatomical model of the different vessels both to initialize and constrain this fast marching, thus eliminating the need for manual selection of seed points. The method is evaluated using data from multiple regions (USA, India, China, Israel) including a variety of scanners (10, 16, 40, 64-slice; Brilliance CT, Philips Healthcare, Cleveland, OH, USA), contrast agent dose, and image resolution. It is fully successful in over 90% of patients and only misses a single vessel in most remaining cases. We also demonstrate its robustness to metal and dental artifacts and anatomical variability. Total processing time is approximately two minutes with no user interaction, which dramatically improves the workflow over existing clinical software. It also reduces patient dose exposure by obviating the need to acquire an unenhanced scan for bone suppression as this can be done by applying the segmentation masks. Keywords: CTA, vessel segmentation 1. INTRODUCTION Extracting vessels from image volumes is a key requirement for the display and analysis of CT angiography studies. Indeed, vessel centerlines are needed to create curved multi-planar reformatted (cMPR) views - where the whole vessel is visible in a single 2D image - or to display vessel cross-sections. Segmented vessel masks are useful for quantitative analysis of pathologies, such as stenoses, plaque or aneurysms. These vessel masks can also be used to remove the bony structures from 3D rendered images, without resorting to image subtraction between a contrast-enhanced and a non- contrast image dataset, thus reducing patient radiation dose and avoiding registration error artifacts. Bone masking is particularly challenging for the vessels in the neck region, since the internal carotid goes through the base of the skull, the vertebral arteries cross through the cervical vertebrae, and the basilar artery closely follows the occipital bone. Vessel extraction can be performed in many different ways, as discussed by Kirbas et al [6]. A widely used approach consists of splitting the problem into two steps. First step involves an approximation of the vessel centerline. It is for instance, defined as the path of minimal cost between user-selected seed points [2,4,7]. Then, this centerline is used to initialize the segmentation of the whole vessel [5]. In other cases, these two steps are iterated, partial segmentation results in helping to estimate the local vessel direction and drive the centerline definition [1,8]. This segmentation step is most often performed using geodesic active surfaces. A major limitation of these methods is that, in order to extract a specific vessel, they typically require user interaction in the form of one [8] or more seed points placed in the vessel to be extracted. This precludes preprocessing the data before human inspection and therefore slows down the clinical workflow. These methods also typically require significant Medical Imaging 2008: Image Processing, edited by Joseph M. Reinhardt, Josien P. W. Pluim, Proc. of SPIE Vol. 6914, 69143R, (2008) 1605-7422/08/$18 · doi: 10.1117/12.770481 Proc. of SPIE Vol. 6914 69143R-1 2008 SPIE Digital Library -- Subscriber Archive Copy