Abstract
The amount of coronary artery calcium (CAC) is a strong and independent predictor of coronary heart disease (CHD). The standard routine for CAC quantification is to perform non-contrasted coronary computed-tomography (CCT) on a patient and present the resulting image to an expert, who then uses this to label CAC in a tedious and time-consuming process. To improve this situation, we present an automatic CAC labeling system with high clinical practicability. In contrast to many other automatic calcium scoring systems, it does not require additional cardiac computed tomography angiography (CCTA) data for artery-specific labeling. Instead, an atlas-based feature approach in combination with a random forest (RF) classifier is used to incorporate fuzzy spatial knowledge from offline data. Overall detection of CAC volume on a test set with 40 patients yields an \(F_1\) score of 0.95 and 1.00 accuracy for risk class assignment. The intraclass correlation coefficient is 0.98 for the left anterior descending artery (LAD), 0.88 for the left circumflex artery (LCX), and 0.98 for the right coronary artery (RCA). The implemented system offers state-of-the-art accuracy with a processing time (< 30 s) by magnitudes lower than comparable systems to be found in the literature.
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Durlak, F., Wels, M., Schwemmer, C., Sühling, M., Steidl, S., Maier, A. (2017). Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_4
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DOI: https://doi.org/10.1007/978-3-319-67389-9_4
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