Coronary artery segmentation in non-contrast cardiac CT using anatomy-informed contrastive learning and synthetic data

Abstract Objective. Non-contrast cardiac CT (NCCT) offers a low-dose, cost-effective alternative to coronary CT angiography (CCTA) for large-scale coronary artery disease screening. However, automatic segmentation on NCCT is severely hindered by poor vessel visibility and a scarcity of annotated datasets. This study aims to overcome these limitations by developing a method for accurate coronary artery segmentation from NCCT images without requiring manual annotations. 

Approach. We propose SynCAS (Synthetic-data-driven Coronary Artery Segmentation), a deep learning framework trained entirely on synthetic data. First, we developed a comprehensive generation pipeline to create a diverse, large-scale synthetic NCCT dataset with perfect ground truth, modeling the physics of NCCT imaging. Second, to address the low contrast-to-noise ratio, we introduced an anatomy-informed contrastive learning strategy. Unlike traditional methods, this strategy utilizes voxel-level pseudo-negative samples guided by anatomical priors, enabling the model to effectively distinguish coronary arteries from visually similar background structures and reduce false positives. 

Main results. The proposed method was evaluated on both a public NCCT dataset and an in-house clinical dataset. Experimental results demonstrate that SynCAS consistently outperforms state-of-the-art unsupervised and domain-adaptation approaches. The model exhibits strong generalization capabilities across different datasets despite being trained without real-world annotations.

Significance. SynCAS provides a robust solution for analyzing coronary arteries in non-contrast imaging, potentially facilitating retrospective analysis and large-scale population screening for cardiovascular risk without the radiation dose and contrast agent risks associated with CCTA. Code and model weights will be available at: https://github.com/Advanced-AI-in-Medicine-and-Physics-Lab/SynCAS.git.