Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction. The proposed method uses a growing strategy and contains three main parts namely, (1) the initial seed detection that automatically detects the root points of the left and right coronary arteries where the ascending aorta meets the coronary arteries, (2) the growing strategy that searches for the neighborhood blocks to decide the existence of coronary arteries with an improved convolutional neural network, and (3) the iterative termination condition that decides whether the growing iteration finishes. The proposed framework is validated using a dataset containing 32 cardiac CTA volumes from different patients for training and testing. Experimental results show that the proposed method obtained a Dice loss ranged from 0.70 to 0.83, which indicates that the new method outperforms the traditional methods such as level set.

中文翻译：

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使用生长算法全自动分割冠状动脉。

目前，心脏计算机断层扫描血管造影（CTA）被广泛应用于冠状动脉疾病的诊断。冠状动脉自动分割在冠状动脉疾病诊断中发挥了重要作用。在这项研究中，我们提出并测试了一种不需要任何人机交互的全自动冠状动脉分割方法。所提出的方法使用生长策略，包含三个主要部分，即（1）初始种子检测，自动检测升主动脉与冠状动脉相遇的左右冠状动脉的根点，（2）生长策略，使用改进的卷积神经网络搜索邻域块以确定冠状动脉的存在，(3) 决定增长迭代是否结束的迭代终止条件。所提出的框架使用包含来自不同患者的 32 个心脏 CTA 卷的数据集进行验证，用于训练和测试。实验结果表明，该方法获得的 Dice loss 介于 0.70 到 0.83 之间，这表明新方法优于水平集等传统方法。