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.

Centerlines, Coronary arteries, Coronary lumen, Data processing, Dice coefficient, Edge weights, Graph cut, Image segmentation, Kernel regression, Manual annotation, Medical imaging, Novel methods, Quantitative evaluation, Semi-automatic segmentation, Symmetric surfaces
Volume 5636
Erasmus MC: University Medical Center Rotterdam

Schaap, M.M, Neefjes, L.A.E, Metz, C.T, van der Giessen, A.G, Weustink, A.C, Mollet, N.R.A, … Niessen, W.J. (2009). Coronary lumen segmentation using graph cuts and robust kernel regression. Retrieved from