title: Coronary lumen segmentation using graph cuts and robust kernel regression.
abstract: This paper presents a novel method for segmenting the coronary lumen in CTA data. The method is based on graph cuts, with edge-weights depending on the intensity of the centerline, and robust kernel regression. A quantitative evaluation in 28 coronary arteries from 12 patients is performed by comparing the semi-automatic segmentations to manual annotations. This evaluation showed that the method was able to segment the coronary arteries with high accuracy, compared to manually annotated segmentations, which is reflected in a Dice coefficient of 0.85 and average symmetric surface distance of 0.22 mm.
authors.affiliation: Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam. michiel.schaap@erasmusmc.nl
authors.forename: Michiel
authors.initials: M
authors.lastname: Schaap
authors.forename: Lisan
authors.initials: L
authors.lastname: Neefjes
authors.forename: Coert
authors.initials: C
authors.lastname: Metz
authors.forename: Alina
authors.initials: A
authors.lastname: van der Giessen
authors.forename: Annick
authors.initials: A
authors.lastname: Weustink
authors.forename: Nico
authors.initials: N
authors.lastname: Mollet
authors.forename: Jolanda
authors.initials: J
authors.lastname: Wentzel
authors.forename: Theo W
authors.initials: TW
authors.lastname: van Walsum
authors.forename: Wiro
authors.initials: W
authors.lastname: Niessen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*