Zeng Ye-Zhan, Zhao Yu-Qian, Tang Ping, Liao Miao, Liang Yi-Xiong, Liao Sheng-Hui, Zou Bei-Ji
School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China.
School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China.
Comput Methods Programs Biomed. 2017 Oct;150:31-39. doi: 10.1016/j.cmpb.2017.07.002. Epub 2017 Jul 22.
Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method.
Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein.
The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction.
The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.
从腹部计算机断层血管造影（CTA）容积中准确分割肝血管对于肝血管分析和活体肝移植非常重要。本文提出了一种新颖的肝血管分割与识别方法。
首先，使用各向异性扩散滤波器在保留血管边界的同时平滑噪声。然后，基于血管结构的梯度对称和反对称模式，分别应用最优方向通量（OOF）和方向通量反对称（OFA）度量来检测肝血管及其边界，并进一步细化血管。接下来，根据血管几何结构，提出一种基于高度脊遍历和叶节点线生长（LNLG）的中心线提取方法用于提取肝血管中心线，并将基于快速行进的强度模型集成到图割（GCs）中以有效分割肝血管。最后，应用距离投票机制分离肝静脉和门静脉。
腹部CTA图像的实验结果表明，所提方法能够有效分割肝血管，平均准确率、灵敏度和特异性分别达到97.7%、79.8%和98.6%，并且在细血管提取方面具有良好性能。
所提方法无需手动选择中心线和血管种子，能够有效分割肝血管并识别肝静脉和门静脉。
