Article

Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data.

Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, USA.
The international journal of cardiovascular imaging (Impact Factor: 2.15). 03/2010; 26(7):829-38. DOI: 10.1007/s10554-010-9608-1
Source: PubMed

ABSTRACT Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (± 15.80)%, 93.54 (± 1.98)%, and 85.27 (± 14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (± 7.78)%, 96.57 (± 4.90)%, and 73.58 (± 8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.

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    ABSTRACT: Accurate quantification of coronary artery calcium provides an opportunity to assess the extent of atherosclerosis disease. Coronary calcification burden has been reported to be associated with cardiovascular risk. Currently, an observer has to identify the coronary calcifications among a set of candidate regions, obtained by thresholding and connected component labeling, by clicking on them. To relieve the observer of such a labor-intensive task, an automated tool is needed that can detect and quantify the coronary calcifications. However, the diverse and heterogeneous nature of the candidate regions poses a significant challenge. In this paper, we investigate a supervised classification-based approach to distinguish the coronary calcifications from all the candidate regions and propose a two-stage, hierarchical classifier for automated coronary calcium detection. At each stage, we learn an ensemble of classifiers where each classifier is a cost-sensitive learner trained on a distinct asymmetrically sampled data subset. We compute the relative location of the calcifications with respect to a heart-centered coordinate system, and also use the neighboring regions of the calcifications to better characterize their properties for discrimination. Our method detected coronary calcifications with an accuracy, sensitivity and specificity of 98.27, 92.07 and 98.62%, respectively, for a testing dataset of non-contrast computed tomography scans from 105 subjects.
    The international journal of cardiovascular imaging 03/2010; 26(7):817-28. · 2.15 Impact Factor

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Jun 5, 2014