  Precise localization of coronary arteries in Computed Tomography (CT) scans
is critical from the perspective of medical assessment of coronary artery
disease. Although various methods exist that offer high-quality segmentation of
coronary arteries in cardiac contrast-enhanced CT scans, the potential of less
invasive, non-contrast CT in this area is still not fully exploited. Since such
fine anatomical structures are hardly visible in this type of medical images,
the existing methods are characterized by high recall and low precision, and
are used mainly for filtering of atherosclerotic plaques in the context of
calcium scoring. In this paper, we address this research gap and introduce a
deep learning algorithm for segmenting coronary arteries in multi-vendor
ECG-gated non-contrast cardiac CT images which benefits from a novel framework
for semi-automatic generation of Ground Truth (GT) via image registration. We
hypothesize that the proposed GT generation process is much more efficient in
this case than manual segmentation, since it allows for a fast generation of
large volumes of diverse data, which leads to well-generalizing models. To
investigate and thoroughly evaluate the segmentation quality based on such an
approach, we propose a novel method for manual mesh-to-image registration,
which is used to create our test-GT. The experimental study shows that the
trained model has significantly higher accuracy than the GT used for training,
and leads to the Dice and clDice metrics close to the interrater variability.
