Automated coronary artery calcium segmentation in cardiac CT using combined probability map and feature analysis - A preliminary report

Coronary artery calcium (CAC) scoring has been adopted in US national guidelines as a diagnostic tool for risk assessment of asymptomatic individuals at risk of atherosclerotic cardiovascular disease which is the leading cause of death worldwide. To enable fast and accurate CAC scoring, we present a fully automated CAC scoring system for non-contrast ECG-gated cardiac CT scans. First, an anatomical model was developed for segmenting important structures in the heart region, including ascending and descending aortas as well as the myocardium, to limit the CAC search region. A novel trained probability map was developed and used for localizing and labeling the candidate calcified plaques based on coronary artery groups, including left main and left anterior descending artery (LMD), left circumflex artery (LCX), and right coronary artery (RCA). Finally, location, size and intensity features of each CAC candidate were used to remove false CAC candidates. This method was trained on 88 scans and tested on 559 scans from the Multi-Ethnic Study of Atherosclerosis (MESA) dataset. The combination of these methods yielded a Pearson correlation of 0.98, an average Dice score of 0.85, and a risk-category accuracy of 0.87. The artery specific detection precision, based on the probability map labelling, was 0.876 for LMD, 0.830 for LCX and 0.997 for RCA. These results indicate the potential of an automated CAC scoring system for easier and improved screening in cardiac CT scans, and a novel method for CAC labelling.