title: Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population.
abstract: We propose and validate a semi-automatic method for lumen segmentation of the carotid bifurcation in computed tomography angiography (CTA). First, the central vessel axis is obtained using path tracking between three user-defined points. Second, starting from this path, the segmentation is automatically obtained using a level set. The cost and speed functions for path tracking and segmentation make use of intensity and homogeneity slice-based image features. The method is validated on a large data set of 234 carotid bifurcations of 129 ischemic stroke patients with atherosclerotic disease. The results are compared to manually obtained lumen segmentations. Parameter optimization is carried out on a subset of 30 representative carotid bifurcations. With the optimized parameter settings the method successfully tracked the central vessel paths in 201 of the remaining 204 bifurcations (99%) which were not part of the training set. Comparison with manually drawn segmentations shows that the average overlap between the method and observers is similar (for the inter-observer set the results were 92% vs. 87% and for the intra-observer set 94% vs. 94%). Therefore the method has potential to replace the manual procedure of lumen segmentation of the atherosclerotic bifurcation in CTA.
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. r.manniesing@erasmusmc.nl
authors.forename: Rashindra
authors.initials: R
authors.lastname: Manniesing
authors.forename: Michiel
authors.initials: M
authors.lastname: Schaap
authors.forename: Sietske
authors.initials: S
authors.lastname: Rozie
authors.forename: Reinhard
authors.initials: R
authors.lastname: Hameeteman
authors.forename: Danijela
authors.initials: D
authors.lastname: Vukadinovic
authors.forename: Aad
authors.initials: A
authors.lastname: van der Lugt
authors.forename: Wiro
authors.initials: W
authors.lastname: Niessen
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D050197:Atherosclerosis / Q000000981:diagnostic imaging*; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*