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arXiv · 2206.12300

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

Yinghui Meng·Zhenglong Du·Chen Zhao·Minghao Dong·Drew Pienta·Zhihui Xu·Weihua Zhou
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Abstract

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.

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Yinghui Meng, Zhenglong Du, Chen Zhao, Minghao Dong, Drew Pienta, Zhihui Xu, Weihua Zhou. 2022-06-24. Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. https://doi.org/10.3233/thc-230278

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