Deep learning‐based whole‐heart segmentation in coronary computed tomography angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non‐contrast‐enhanced CT (NCCT) scanning would be valuable, but defining a manual reference standard that would allow training a deep learning‐based method for whole‐heart segmentation in NCCT is challenging, if not impossible. In this work, we leverage dual‐energy information provided by a dual‐layer detector CT scanner to obtain a reference standard in virtual non‐contrast (VNC) CT images mimicking NCCT images, and train a three‐dimensional (3D) convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images.

中文翻译：

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从双能量信息中进行深度学习，以在双能量和单能量非增强性心脏CT中进行全心脏分割。

冠状动脉计算机断层扫描血管造影（CCTA）中基于深度学习的全心分割可提取定量成像测量数据，以预测心血管风险。在仅接受非增强CT（NCCT）扫描的患者中自动提取这些措施将很有价值，但是，如果能够定义一种手动参考标准，以允许在NCCT中训练基于深度学习的全心分割方法，则存在挑战。不是不可能。在这项工作中，我们利用双层检测器CT扫描仪提供的双能信息在模仿NCCT图像的虚拟非对比（VNC）CT图像中获取参考标准，并训练三维（3D）卷积神经网络（CNN）来分割VNC和NCCT图像。