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.

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Information Processing in Medical Imaging
Erasmus MC: University Medical Center Rotterdam

Schaap, M., Neefjes, L., Metz, C., van der Giessen, A., Weustink, A., Mollet, N., … Niessen, W. (2009). Coronary lumen segmentation using graph cuts and robust kernel regression. In Information Processing in Medical Imaging (Vol. 21, pp. 528–539). Retrieved from