Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT

Abstract

Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing timeconsuming intermediate CAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings.

Deep Learning for Cardiac Image Segmentation: A Review

“…These aforementioned approaches all follow the same workflow where the CAC is first identified and then quantified. An alternative approach is to circumvent the intermediate segmentation and to perform direct quantification, such as in (Cano-Espinosa et al, 2018;de Vos et al, 2019), which have proven that this approach is effective and promising.…”

Preparing Medical Imaging Data for Machine Learning

“…Automatic lesion detection by using machine learning has been applied to many imaging modalities and includes detection of pneumothorax (10,11), intracranial hemorrhage (12), Alzheimer disease (13), and urinary stones (14). Automatic quantification of medical images includes assessing skeletal maturity on pediatric hand radiographs (15), coronary calcium scoring on CT images (16), prostate classification at MRI (17), breast density at mammography (18), and ventricle segmentation at cardiac MRI (19,20). Yet substantial implementation and regulatory challenges have made application of AI models in clinical practice difficult and limited the potential of these advancements.…”

Left ventricle segmentation in the era of deep learning

“…CNNs typically operate on images, and provide one prediction per image sample ( Figure 1), e.g., an image class label or quantitation of disease burden. 2 These networks contain a large number of parameters, which can be optimized or trained by repeatedly providing training samples and adjusting network parameters to minimize the discrepancy between predicted values and desired output values.…”