Purpose

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.

Methods

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.

Results

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.

Conclusion

The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.

中文翻译：

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结合多视图 2D 卷积神经网络的主动脉计算机断层扫描血管造影的 3D 自动分割。

 目的

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结构、识别病理情况以及在血管手术中进行术前规划至关重要。为了克服手动和半自动分割工具的限制，我们应用基于深度学习的管道自动分割主动脉腔的 CTA 扫描，从升主动脉到髂动脉，考虑 3D 空间相干性。

 方法

首先使用卷积神经网络 (CNN) 在整个子采样 CTA 体积中粗略分割和定位主动脉，然后使用三个单视图 CNN 在更高的分辨率下从轴向、矢状和冠状平面有效分割主动脉腔。解决。最后，整合三个正交网络的预测以获得具有空间相干性的分割。

 结果

用于识别主动脉腔的粗分割实现了 0.92 ± 0.01 的 Dice 系数 (DSC)。单视图轴向、矢状和冠状 CNN 提供的 DSC 分别为 0.92 ± 0.02、0.92 ± 0.04 和 0.91 ± 0.02。多视图集成在 10 次 CTA 扫描的测试集上提供了 0.93 ± 0.02 的 DSC 和 0.80 ± 0.26 mm 的平均表面距离。地面实况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，采用的流程大约需要 25 ± 1 s 才能获得最终分割。

 结论

所取得的结果表明，所提出的管道可以有效地定位和分割动脉瘤受试者的主动脉腔。