Contrastive Coronary Artery Calcification Image Retrieval in Computed Tomography

Cardiovascular diseases are one of the main causes of death in the world. The predominant form of cardiovascular disease is coronary artery disease. Coronary artery calcium scanning is a non-contrast computed tomography exam that is considered the most reliable predictor of coronary events. Deep learning models have been developed for the segmentation of coronary artery calcium but the results have limited interpretability due to the black-box nature of these models. This work proposes an image retrieval pipeline based on a supervised contrastive framework that is capable of enhancing this interpretability by providing similar visual examples of coronary calcifications. In the COCA dataset, it is shown that this retrieval presents a label precision of 0.944 ± 0.230 regarding artery labels of retrieved images, with moderate similarity in terms of calcification area and Agatston score. It is also shown that the retrieval can be used to correct a deep CAC segmentation model by passing predictions from a segmentation model through the retrieval system, improving robustness and explainability.Clinical relevance- This study enhances CAC segmentation through image retrieval, improving both explainability and artery-specific labeling. By providing clinicians with more interpretable and anatomically accurate results, our approach aims to increase confidence in AI-assisted diagnostics leading to better-informed clinical decision-making in coronary artery disease diagnosis.