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Proceedings Paper

Automated aortic calcification detection in low-dose chest CT images
Author(s): Yiting Xie; Yu Maw Htwe; Jennifer Padgett; Claudia Henschke; David Yankelevitz; Anthony P. Reeves
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Paper Abstract

The extent of aortic calcification has been shown to be a risk indicator for vascular events including cardiac events. We have developed a fully automated computer algorithm to segment and measure aortic calcification in low-dose noncontrast, non-ECG gated, chest CT scans. The algorithm first segments the aorta using a pre-computed Anatomy Label Map (ALM). Then based on the segmented aorta, aortic calcification is detected and measured in terms of the Agatston score, mass score, and volume score. The automated scores are compared with reference scores obtained from manual markings. For aorta segmentation, the aorta is modeled as a series of discrete overlapping cylinders and the aortic centerline is determined using a cylinder-tracking algorithm. Then the aortic surface location is detected using the centerline and a triangular mesh model. The segmented aorta is used as a mask for the detection of aortic calcification. For calcification detection, the image is first filtered, then an elevated threshold of 160 Hounsfield units (HU) is used within the aorta mask region to reduce the effect of noise in low-dose scans, and finally non-aortic calcification voxels (bony structures, calcification in other organs) are eliminated. The remaining candidates are considered as true aortic calcification. The computer algorithm was evaluated on 45 low-dose non-contrast CT scans. Using linear regression, the automated Agatston score is 98.42% correlated with the reference Agatston score. The automated mass and volume score is respectively 98.46% and 98.28% correlated with the reference mass and volume score.

Paper Details

Date Published: 20 March 2014
PDF: 8 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350P (20 March 2014); doi: 10.1117/12.2043810
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Yu Maw Htwe, Icahn School of Medicine at Mount Sinai Hospital (United States)
Jennifer Padgett, Cornell Univ. (United States)
Claudia Henschke, Icahn School of Medicine at Mount Sinai Hospital (United States)
David Yankelevitz, Icahn School of Medicine at Mount Sinai Hospital (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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