title: Semiautomatic carotid lumen segmentation for quantification of lumen geometry in multispectral MRI.
abstract: Quantitative information about the geometry of the carotid artery bifurcation is relevant for investigating the onset and progression of atherosclerotic disease. This paper proposes an automatic approach for quantifying the carotid bifurcation angle, carotid area ratio, carotid bulb size and the vessel tortuosity from multispectral MRI. First, the internal and external carotid centerlines are determined by finding a minimum cost path between user-defined seed points where the local costs are based on medialness and intensity. The minimum cost path algorithm is iteratively applied after curved multi-planar reformatting to refine the centerline. Second, the carotid lumen is segmented using a topology preserving geodesic active contour which is initialized by the extracted centerlines and steered by the MR intensities. Third, the bifurcation angle and vessel tortuosity are automatically extracted from the segmented lumen. The methods for centerline tracking and lumen segmentation are evaluated by comparing their accuracy to the inter- and intra-observer variability on 48 datasets (96 carotid arteries) acquired as part of a longitudinal population study. The evaluation reveals that 94 of 96 carotid arteries are segmented successfully. The distance between the tracked centerlines and the reference standard (0.33 mm) is similar to the inter-observer variation (0.32 mm). The lumen segmentation accuracy (average DSC=0.89, average mean absolute surface distance=0.31 mm) is close to the inter-observer variation (average dice=0.92, average mean surface distance=0.23 mm). The correlation coefficient of manually and automaticly derived bifurcation angle, carotid proximal area ratio, carotid proximal bulb size and vessel totuosity quantifications are close to the correlation of these measures between observers. This demonstrates that the automated method can be used for replacing manual centerline annotation and manual contour drawing for lumen segmentation in MRIs data prior to quantifying the carotid bifurcation geometry.
authors: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands. h.tang@tudelft.nl
authors: Hui
authors: H
authors: Tang
authors: Theo
authors: T
authors: van Walsum
authors: Robbert S
authors: RS
authors: van Onkelen
authors: Reinhard
authors: R
authors: Hameeteman
authors: Stefan
authors: S
authors: Klein
authors: Michiel
authors: M
authors: Schaap
authors: Fufa L
authors: FL
authors: Tori
authors: Quirijn J A
authors: QJ
authors: van den Bouwhuijsen
authors: Jacqueline C M
authors: JC
authors: Witteman
authors: Aad
authors: A
authors: van der Lugt
authors: Lucas J
authors: LJ
authors: van Vliet
authors: Wiro J
authors: WJ
authors: Niessen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002339:Carotid Arteries / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity