Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Phys Med Biol. 2021 Jul 19;66(15). doi: 10.1088/1361-6560/ac0d8e.
Vessel centerline extraction from x-ray angiography images is essential for vessel structure analysis in the diagnosis of coronary artery disease. However, complete and continuous centerline extraction remains a challenging task due to image noise, poor contrast, and complexity of vessel structure. Thus, an iterative multi-path search framework for automatic vessel centerline extraction is proposed. First, the seed points of the vessel structure are detected and sorted by confidence. With the ordered seed points, multi-bifurcation centerline is searched through multi-path propagation of wavefront and accumulated voting. Finally, the centerline is further extended piecewise by wavefront propagation on the basis of keypoint detection. The latter two steps are performed alternately to obtain the final centerline result. The proposed method is qualitatively and quantitatively evaluated on 1260 synthetic images and 50 clinical angiography images. The results demonstrate that our method has a highF1score of 87.8% ± 2.7% for the angiography images and achieves accurate and continuous results of vessel centerline extraction.
从 X 射线血管造影图像中提取血管中心线对于冠心病的血管结构分析至关重要。然而,由于图像噪声、对比度差和血管结构的复杂性,完整而连续的中心线提取仍然是一项具有挑战性的任务。因此,提出了一种迭代多路径搜索框架,用于自动提取血管中心线。首先,通过置信度检测和排序来检测血管结构的种子点。利用有序的种子点,通过波前的多路径传播和累积投票来搜索多分支中心线。最后,基于关键点检测,通过波前传播对中心线进行分段扩展。后两步交替进行,以获得最终的中心线结果。在 1260 张合成图像和 50 张临床血管造影图像上对所提出的方法进行了定性和定量评估。结果表明,我们的方法在血管造影图像上的 F1 分数为 87.8%±2.7%,实现了血管中心线提取的准确和连续结果。