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Accurate extraction of coronary arteries from invasive coronary angiography(ICA) is important in clinical decision-making for the diagnosis and riskstratification of coronary artery disease (CAD). In this study, we develop amethod using deep learning to automatically extract the coronary artery lumen.Methods. A deep learning model U-Net 3+, which incorporates the full-scale skipconnections and deep supervisions, was proposed for automatic extraction ofcoronary arteries from ICAs. Transfer learning and a hybrid loss function wereemployed in this novel coronary artery extraction framework. Results. A dataset containing 616 ICAs obtained from 210 patients was used. In the technicalevaluation, the U-Net 3+ achieved a Dice score of 0.8942 and a sensitivity of0.8735, which is higher than U-Net ++ (Dice score: 0.8814, the sensitivity of0.8331) 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 segmentationframeworks for the automatic extraction of the coronary arteries from ICAs.This result suggests great promise for clinical use.

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms