2009-09-21
Coronary lumen segmentation using graph cuts and robust kernel regression
Publication
Publication
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.
| Additional Metadata | |
|---|---|
| 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 | |
| 978-3-642-02497-9 | |
| hdl.handle.net/1765/17397 | |
| Volume 5636 | |
| Organisation | Erasmus MC: University Medical Center Rotterdam |
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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 http://hdl.handle.net/1765/17397
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