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Abstract：
The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence.
A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence.
The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation.
The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.
A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence.
The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation.
The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.
摘要：
对比增强计算机断层扫描血管造影（CTA）的定量分析对于评估主动脉解剖至关重要。识别病理，并在血管外科手术中进行术前规划。为了克服手动和半自动分割工具的局限性，我们应用基于深度学习的管道来自动分割主动脉腔的CTA扫描，从升主动脉到髂动脉,考虑三维空间相干性。
第一个卷积神经网络（CNN）用于粗略地分割和定位整个子采样CTA体积中的主动脉，然后使用三个单视图CNN从轴向有效地分割主动脉腔，矢状,和冠状平面在更高的分辨率。最后,对三个正交网络的预测进行集成，以获得具有空间相干性的分割。
为了识别主动脉腔而进行的粗分割实现了0.92±0.01的Dice系数(DSC)。单视图轴向，矢状,和冠状CNN的DSC分别为0.92±0.02、0.92±0.04和0.91±0.02。多视图积分在10次CTA扫描的测试组上提供0.93±0.02的DSC和0.80±0.26mm的平均表面距离。地面真值数据集的生成大约需要150小时，整个训练过程需要18小时。在预测阶段，采用的管道需要大约25±1s才能得到最终的分割。
获得的结果表明，所提出的管道可以有效地定位和分割动脉瘤患者的主动脉腔。
第一个卷积神经网络（CNN）用于粗略地分割和定位整个子采样CTA体积中的主动脉，然后使用三个单视图CNN从轴向有效地分割主动脉腔，矢状,和冠状平面在更高的分辨率。最后,对三个正交网络的预测进行集成，以获得具有空间相干性的分割。
为了识别主动脉腔而进行的粗分割实现了0.92±0.01的Dice系数(DSC)。单视图轴向，矢状,和冠状CNN的DSC分别为0.92±0.02、0.92±0.04和0.91±0.02。多视图积分在10次CTA扫描的测试组上提供0.93±0.02的DSC和0.80±0.26mm的平均表面距离。地面真值数据集的生成大约需要150小时，整个训练过程需要18小时。在预测阶段，采用的管道需要大约25±1s才能得到最终的分割。
获得的结果表明，所提出的管道可以有效地定位和分割动脉瘤患者的主动脉腔。
