Share Email Print

Proceedings Paper

Automated coronary artery calcification detection on low-dose chest CT images
Author(s): Yiting Xie; Matthew D. Cham; Claudia Henschke; David Yankelevitz; Anthony P. Reeves
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Coronary artery calcification (CAC) measurement from low-dose CT images can be used to assess the risk of coronary artery disease. A fully automatic algorithm to detect and measure CAC from low-dose non-contrast, non-ECG-gated chest CT scans is presented. Based on the automatically detected CAC, the Agatston score (AS), mass score and volume score were computed. These were compared with scores obtained manually from standard-dose ECG-gated scans and low-dose un-gated scans of the same patient. The automatic algorithm segments the heart region based on other pre-segmented organs to provide a coronary region mask. The mitral valve and aortic valve calcification is identified and excluded. All remaining voxels greater than 180HU within the mask region are considered as CAC candidates. The heart segmentation algorithm was evaluated on 400 non-contrast cases with both low-dose and regular dose CT scans. By visual inspection, 371 (92.8%) of the segmentations were acceptable. The automated CAC detection algorithm was evaluated on 41 low-dose non-contrast CT scans. Manual markings were performed on both low-dose and standard-dose scans for these cases. Using linear regression, the correlation of the automatic AS with the standard-dose manual scores was 0.86; with the low-dose manual scores the correlation was 0.91. Standard risk categories were also computed. The automated method risk category agreed with manual markings of gated scans for 24 cases while 15 cases were 1 category off. For low-dose scans, the automatic method agreed with 33 cases while 7 cases were 1 category off.

Paper Details

Date Published: 20 March 2014
PDF: 9 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350F (20 March 2014); doi: 10.1117/12.2043840
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Matthew D. Cham, Icahn School of Medicine at Mount Sinai Hospital (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)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?