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Proceedings Paper

Coronary artery calcification identification and labeling in low-dose chest CT images
Author(s): Yiting Xie; Shuang Liu; Albert Miller; Jeffrey A. Miller; Steven Markowitz; Ali Akhund; Anthony P. Reeves
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Paper Abstract

A fully automated computer algorithm has been developed to evaluate coronary artery calcification (CAC) from lowdose CT scans. CAC is identified and evaluated in three main coronary artery groups: Left Main and Left Anterior Descending Artery (LM + LAD) CAC, Left Circumflex Artery (LCX) CAC, and Right Coronary Artery (RCA) CAC. The artery labeling is achieved by segmenting all CAC candidates in the heart region and applying geometric constraints on the candidates using locally pre-identified anatomy regions. This algorithm was evaluated on 1,359 low-dose ungated CT scans, in which each artery CAC content was categorically visually scored by a radiologist into none, mild, moderate and extensive. The Spearman correlation coefficient R was used to assess the agreement between three automated CAC scores (Agatston-weighted, volume, and mass) and categorical visual scores. For Agatston-weighted automated scores, R was 0.87 for total CAC, 0.82 for LM + LAD CAC, 0.66 for LCX CAC and 0.72 for RCA CAC; results using volume and mass scores were similar. CAC detection sensitivities were: 0.87 for total, 0.82 for LM + LAD, 0.65 for LCX and 0.74 for RCA. To assess the impact of image noise, the dataset was further partitioned into three subsets based on heart region noise level (low<=80HU, medium=(80HU, 110HU], high>110HU). The low and medium noise subsets had higher sensitivities and correlations than the high noise subset. These results indicate that location specific heart risk assessment is possible from low-dose chest CT images.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340L (3 March 2017); doi: 10.1117/12.2254125
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Shuang Liu, Cornell Univ. (United States)
Albert Miller, Queen's College, City Univ. of New York (United States)
Jeffrey A. Miller, School of Medicine, Rutgers Univ. (United States)
Steven Markowitz, Queen's College, City Univ. of New York (United States)
Ali Akhund, Queen's College, City Univ. of New York (United States)
Anthony P. Reeves, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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