title: Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA.
abstract: This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.
authors.affiliation: Departments of Medical Informatics and Radiology, Erasmus MC—University Medical Center Rotterdam, The Netherlands. michiel.schaap@erasmusmc.nl
authors.forename: Michiel
authors.initials: M
authors.lastname: Schaap
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
authors.forename: Lisan
authors.initials: L
authors.lastname: Neefjes
authors.forename: Coert
authors.initials: C
authors.lastname: Metz
authors.forename: Ermanno
authors.initials: E
authors.lastname: Capuano
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
authors.forename: Wiro
authors.initials: W
authors.lastname: Niessen
mesh_terms: D000465:Algorithms; D017023:Coronary Angiography / Q000295:instrumentation / Q000379:methods*; D003331:Coronary Vessels / Q000033:anatomy & histology*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D016014:Linear Models; D017711:Nonlinear Dynamics; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*