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

• Published in: arXiv.org 2022-06
• Main Authors: Meng, Yinghui, Du, Zhenglong, Chen, Zhao, Dong, Minghao, Pienta, Drew, Xu, Zhihui, Zhou, Weihua
• Format: Article
• Language: English
• Subjects: Angiography; Arteries; Cardiovascular disease; Coronary artery disease; Coronary vessels; Decision making; Deep learning; Segmentation; Sensitivity; Stents
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2206.12300

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.