title: Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets.
abstract: We present a new level-set based method to segment and quantify stenosed internal carotid arteries (ICAs) in 3D contrast-enhanced computed tomography angiography (CTA). Within these data sets it is a difficult task to evaluate the degree of stenoses deterministically even for the experienced physician because the actual vessel lumen is hardly distinguishable from calcified plaque and there is no sharp border between lumen and arterial wall. According to our knowledge no commercially available software package allows the detection of the boundary between lumen and plaque components. Therefore in the clinical environment physicians have to perform the evaluation manually. This approach suffers from both intra- and inter-observer variability. The limitation of the manual approach requires the development of a semi-automatic method that is able to achieve deterministic segmentation results of the internal carotid artery via level-set techniques. With the new method different kinds of plaques were almost completely excluded from the segmented regions. For an objective evaluation we also studied the method's performance with four different phantom data sets for which the ground truth of the degree of stenosis was known a priori. Finally, we applied the method to 10 ICAs and compared the obtained segmentations with manual measurements of three physicians.
authors.affiliation: Institute of Pattern Recognition, Department of Computer Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 3, D-91058 Erlangen, Germany. scherl@informatik.uni-erlangen.de
authors.forename: Holger
authors.initials: H
authors.lastname: Scherl
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Marcus
authors.initials: M
authors.lastname: Prümmer
authors.forename: Michael
authors.initials: M
authors.lastname: Lell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D002533:Cerebral Angiography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D014584:User-Computer Interface