Conclusion

The proposed pipeline enables rapid and accurate coronary centerline extraction, achieving high agreement with expert annotations while requiring minimal computational resources. The fully automated nature of our approach eliminates the need for manual interaction, making it ideal for large-scale studies. This automation accelerates stenosis and plaque characterization workflows, supporting timely and effective patient care in cardiology.

Our approach has limitations, notably the reduced resolution from downsampling images due to computational constraints, the relatively small training dataset size and the dependency on external software for cardiac structures delineation. Future work will explore 3D architectures, larger training cohorts, validation on external datasets and the integration of aortic segmentation into the deep learning pipeline to create a fully end-to-end solution.