  Accurate extraction of coronary arteries from invasive coronary angiography
(ICA) is important in clinical decision-making for the diagnosis and risk
stratification of coronary artery disease (CAD). In this study, we develop a
method using deep learning to automatically extract the coronary artery lumen.
Methods. A deep learning model U-Net 3+, which incorporates the full-scale skip
connections and deep supervisions, was proposed for automatic extraction of
coronary arteries from ICAs. Transfer learning and a hybrid loss function were
employed in this novel coronary artery extraction framework. Results. A data
set containing 616 ICAs obtained from 210 patients was used. In the technical
evaluation, the U-Net 3+ achieved a Dice score of 0.8942 and a sensitivity of
0.8735, which is higher than U-Net ++ (Dice score: 0.8814, the sensitivity of
0.8331) and U-net (Dice score: 0.8799, the sensitivity of 0.8305). Conclusion.
Our study demonstrates that the U-Net 3+ is superior to other segmentation
frameworks for the automatic extraction of the coronary arteries from ICAs.
This result suggests great promise for clinical use.
