论文标题
从双能量信息中深度学习双能和单能非对抗性心脏CT中的全心脏分割
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
论文作者
论文摘要
冠状动脉CT血管造影（CCTA）中基于深度学习的全心脏分割允许提取心血管风险预测的定量成像指标。在仅接受非对比增强CT（NCCT）扫描的患者中，自动提取这些措施是有价值的。在这项工作中，我们利用双层检测器CT扫描仪提供的信息来获得模仿NCCT图像的虚拟非对比度（VNC）CT图像中的参考标准，并训练3D卷积神经网络（CNN）以进行VNC分割以及NCCT图像。将双层检测器CT扫描仪上的对比度增强的采集重建为CCTA和完美对齐的VNC图像。在每个CCTA图像中，获得了左心室（LV）心肌，LV腔，右心室，左心庭，右心房，上升主动脉和肺动脉躯干的手动参考分割，并将其传播至相应的VNC图像。这些VNC图像和参考分割用于在VNC图像或NCCT图像中自动分割训练3D CNN。 VNC图像中的自动分割与参考分割显示良好一致，平均骰子相似系数为0.897 \ pm 0.034，平均对称表面距离为1.42 \ pm 0.45 mm。自动NCCT和参考CCTA分割之间的体积差异[95％置信区间]为-19 [-67; 30] ML用于LV心肌，-25 [-78; 29] ML用于LV腔，-29 [-73; 14] ML右心室，-20 [-62; 21]毫升左心房，-19 [-73; 34] ML分别用于右心房。在214个（74％）的NCCT图像中，来自独立的多供应商多中心集，两个观察者同意自动分割大多是准确或更高的。此方法可以量化NCCT图像的其他心脏测量，以改善心血管风险预测。
Deep learning-based whole-heart segmentation in coronary CT 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. In this work, we leverage 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 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquisitions on a dual-layer detector CT scanner were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs for automatic segmentation in either VNC images or NCCT images. Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average Dice similarity coefficient of 0.897 \pm 0.034 and an average symmetric surface distance of 1.42 \pm 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29 [-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19 [-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from an independent multi-vendor multi-center set, two observers agreed that the automatic segmentation was mostly accurate or better. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction.
