School of Computer Science, Kyungil University, Gyeongsan 38428, Korea.
Sensors (Basel). 2021 Sep 10;21(18):6087. doi: 10.3390/s21186087.
Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust 'vesselness' measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature.
在冠状动脉计算机断层扫描（CT）血管造影中提取冠状动脉是定量冠状动脉病变的前提。在这项研究中，我们提出了一种结合深度卷积神经网络（DNN）和粒子滤波方法的跟踪方法，用于从 3D CT 图像中识别从冠状动脉口到每个远端的轨迹。粒子滤波器作为一种非线性逼近器，是跟踪这种细而细长结构的合适跟踪框架；然而，对于提取冠状动脉中心线，稳健的“血管”测量是必不可少的。重要的是，我们使用 DNN 来使用补丁图像稳健地测量血管，并且我们将 softmax 值集成到我们的粒子滤波框架中的似然函数中。切向补丁代表圆形冠状动脉的横截面。因此，假设 2D 切向补丁包含足够的冠状动脉特征，并且使用 2D 补丁可显著降低计算复杂度。由于冠状动脉血管系统有多个分支，因此我们还通过聚类粒子位置来模拟一种检测分支点的方法。将所提出的方法与三个商业工作站和两个学术文献中的传统方法进行了比较。
