Coronary Lumen Segmentation Using Graph Cuts and Robust Kernel Regression

Authors:
Michiel Schaap;Lisan Neefjes;Coert Metz;Alina Giessen;Annick Weustink;Nico Mollet;Jolanda Wentzel;Theo Walsum;Wiro Niessen
Affiliations:
Department of Medical Informatics, and Department of Radiology,;Department of Radiology, and Department of Cardiology, Thoraxcenter,;Department of Medical Informatics, and Department of Radiology,;Department of Biomedical Engineering, Erasmus MC - University Medical Center Rotterdam,;Department of Radiology, and Department of Cardiology, Thoraxcenter,;Department of Radiology, and Department of Cardiology, Thoraxcenter,;Department of Biomedical Engineering, Erasmus MC - University Medical Center Rotterdam,;Department of Medical Informatics, and Department of Radiology,;Department of Medical Informatics, and Department of Radiology,
Venue:
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Year:
2009

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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.