Contrastive Coronary Artery Calcification Image Retrieval in Computed Tomography.

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiovascular diseases, particularly coronary artery disease, are leading global causes of mortality.
• Coronary artery calcium (CAC) scanning is a crucial non-contrast CT exam for predicting coronary events.
• Current deep learning models for CAC segmentation lack interpretability due to their black-box nature.

Purpose of the Study:

• To develop an interpretable image retrieval pipeline for coronary artery calcium.
• To enhance the explainability of deep learning models in CAC segmentation.
• To provide clinicians with visually similar examples of coronary calcifications.

Main Methods:

• Implementation of a supervised contrastive framework for image retrieval.
• Utilizing the COCA dataset for evaluating the retrieval pipeline.
• Integrating the retrieval system with deep CAC segmentation models.

Main Results:

• Achieved a label precision of 0.944 ± 0.230 for artery labels in retrieved images.
• Demonstrated moderate similarity in calcification area and Agatston score.
• Showcased the retrieval system's ability to correct and improve deep CAC segmentation models, enhancing robustness and explainability.

Conclusions:

• The proposed image retrieval pipeline significantly enhances the interpretability of CAC segmentation.
• The system improves artery-specific labeling and provides anatomically accurate results.
• This approach aims to increase clinician confidence in AI-assisted diagnostics for coronary artery disease.