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

Research output: Contribution to journalArticleAcademic

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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.
Original languageUndefined/Unknown
Pages (from-to)528-539
Number of pages12
JournalLecture Notes in Computer Science
Volume5636
DOIs
Publication statusPublished - 2009

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