Rapid Right Coronary Artery Extraction from CT Images via Global-Local Deep Learning Method Based on GhostNet

The right coronary artery plays a crucial role in cardiac function and its accurate extraction and 3D reconstruction from CT images are essential for diagnosing and treating coronary artery disease. This study proposes a novel, automated, deep learning pipeline that integrates a transformer-based network with GhostNet to improve segmentation and 3D reconstruction. The dataset comprised CT images from 32 patients, with the segmentation model effectively extracting vascular cross-sections, achieving an F1 score of 0.887 and an Intersection over Union of 0.797. Meanwhile, the proposed model achieved an inference speed of 7.03 ms, outperforming other state-of-the-art networks used for comparison, making it highly suitable for real-time clinical applications. Compared to conventional methods, the proposed approach demonstrates superior segmentation performance while maintaining computational efficiency. The results indicate that this framework has the potential to significantly improve diagnostic accuracy and interventional planning for coronary artery disease. Future work will focus on expanding dataset diversity, refining real-time processing capabilities, and extending the methodology to other vascular structures.