Deep Learning Meets Particle Swarm Optimization For Aortic Valve Calcium Scoring From Cardiac Computed Tomography

Aortic stenosis is the most common primary valvular pathology requiring surgical or transcatheter intervention in developed countries. Quantification of aortic valve calcification with cardiac computed tomography (CCT) is used for assessment of aortic stenosis severity, disease progression and prediction of major cardiovascular events. The calcium deposits, however, commonly appear in different regions of the aorta and heart, leading to false-positive regions, and to an incorrectly calculated aortic valve calcium score. We tackle the issue of pruning such false-positive regions from CCT scans, and introduce a particle swarm optimization (PSO) algorithm for this task. In our approach, PSO optimizes the radius while benefiting from the evolved position of a sphere which would embrace those calcifications that are positioned near the aortic valve. Our experimental study, performed over 30 non-contrast CCT scans, showed that our results are in strong agreement with the experienced human reader, and indicate the potential of PSO in data-driven pruning of false-positive calcifications which are positioned in other parts of the aorta and heart. Additionally, PSO outperformed a geometrical-based approach for this task.