Deep Learning based CT-scan Coronary Artery Segmentation and Calcium Scoring

Abstract

Coronary artery disease (CAD), primarily driven by atherosclerosis, poses significant health risks, contributing to a rising mortality rate globally. This study introduces a deep learning framework designed for the automated segmentation of coronary arteries and quantification of coronary artery calcium (CAC) from CT scans, facilitating improved risk stratification in patients. Leveraging data from the National Lung Screening Trial, we developed a three-step model that includes heart localization, coronary calcium segmentation, and calcium scoring. Various configurations of the UNet architecture were employed, with the Extended UNet utilizing an autoencoder achieving the highest validation performance, reflected by an Intersection over Union (IoU) score of 0.78 and an F1 score of 0.83. The model's efficacy was validated against manually segmented masks, showcasing its potential for accurate risk assessment based on CAC scores. This automated approach significantly reduces the time and expertise required for traditional calcium scoring, enabling rapid and reliable assessments in clinical settings. Our findings indicate that the deep learning system can effectively classify patients into risk categories, underscoring its potential utility in enhancing the management of CAD and improving patient outcomes. This research highlights the feasibility of integrating advanced computational techniques into routine clinical practice, paving the way for more efficient cardiovascular risk stratification.