Coronary Lumen Segmentation Using Graph Cuts and Robust Kernel Regression

Michiel Schaap, Lisan Neefjes, Coert Metz, Alina Giessen, A.C. Weustink, Nico Mollet, Jolanda Wentzel, Theo van Walsum, Wiro Niessen

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51 Citations (Scopus)


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.
Original languageUndefined/Unknown
Pages (from-to)528-539
Number of pages12
JournalLecture Notes in Computer Science
Publication statusPublished - 2009

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