论文标题
使用深度学习在侵入性冠状动脉造影中自动提取冠状动脉
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms
论文作者
论文摘要
从侵入性冠状动脉造影（ICA）中准确提取冠状动脉（ICA）对于临床决策而言，对于冠状动脉疾病的诊断和风险分层（CAD）很重要。在这项研究中，我们开发了一种使用深度学习来自动提取冠状动脉腔的方法。方法。提出了一种深度学习模型U-NET 3+，其中包含了全面的跳过连接和深度的监督，以自动从ICAS中自动提取冠状动脉。在这个新型的冠状动脉提取框架中采用了转移学习和混合损失功能。结果。使用了一个包含从210名患者获得的616个ICA的数据集。在技术评估中，U-NET 3+的骰子得分为0.8942，灵敏度为0.8735，高于U-NET ++（骰子得分：0.8814：0.8814，灵敏度为0.8331）和U-NET（骰子得分：0.8799，敏感性为0.8305）。结论。我们的研究表明，U-Net 3+优于其他分割框架，用于自动从ICA中提取冠状动脉。该结果表明了临床使用的巨大希望。
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.
