Coronary lumen segmentation using graph cuts and robust kernel regression.
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.
Schaap
Michiel
M
Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam. michiel.schaap@erasmusmc.nl
Neefjes
Lisan
L
Metz
Coert
C
van der Giessen
Alina
A
Weustink
Annick
A
Mollet
Nico
N
Wentzel
Jolanda
J
van Walsum
Theo W
TW
Niessen
Wiro
W
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*