论文标题
多分辨率3D卷积神经网络，用于心脏CT血管造影扫描中自动冠状动脉中心线提取
Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans
论文作者
论文摘要
我们提出了一个基于深度学习的自动冠状动脉树中心线跟踪器（Aucotrack），该跟踪器通过Wolterink（Arxiv：1810.03143）扩展了血管跟踪器。在多尺度3D输入上运行的双途径卷积神经网络（CNN）预测冠状动脉的方向以及分叉的存在。类似的多尺度双途径3D CNN经过训练，以识别冠状动脉终点以终止跟踪过程。根据分叉检测得出两个或多个延续方向。迭代跟踪器仅根据基于模型的心脏分割而得出的两个左右冠状动脉树检测整个左右冠状动脉树。 3D CNN在由43个CCTA扫描组成的专有数据集上进行了培训。相对于精制的手动分割，获得的平均灵敏度为87.1％，临床相关的重叠率为89.1％。此外，使用MICCAI 2008冠状动脉跟踪挑战（CAT08）培训和测试数据集对算法进行基准测试并评估其概括。获得的平均重叠为93.6％，临床相关的重叠获得了96.4％。所提出的方法比CAT08数据集上的当前最新自动中心线提取技术获得了更好的重叠得分，其血管检测率为95％。
We propose a deep learning-based automatic coronary artery tree centerline tracker (AuCoTrack) extending the vessel tracker by Wolterink (arXiv:1810.03143). A dual pathway Convolutional Neural Network (CNN) operating on multi-scale 3D inputs predicts the direction of the coronary arteries as well as the presence of a bifurcation. A similar multi-scale dual pathway 3D CNN is trained to identify coronary artery endpoints for terminating the tracking process. Two or more continuation directions are derived based on the bifurcation detection. The iterative tracker detects the entire left and right coronary artery trees based on only two ostium landmarks derived from a model-based segmentation of the heart. The 3D CNNs were trained on a proprietary dataset consisting of 43 CCTA scans. An average sensitivity of 87.1% and clinically relevant overlap of 89.1% was obtained relative to a refined manual segmentation. In addition, the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08) training and test datasets were used to benchmark the algorithm and to assess its generalization. An average overlap of 93.6% and a clinically relevant overlap of 96.4% were obtained. The proposed method achieved better overlap scores than the current state-of-the-art automatic centerline extraction techniques on the CAT08 dataset with a vessel detection rate of 95%.
