目的 肺内血管形态结构的改变是慢性阻塞性肺疾病（慢阻肺）的一种重要病理改变。针对慢阻肺中肺血管疾病的定量评估问题，提出一种基于各向异性连续最大流的肺内血管自动分割方法，并定量分析不同半径的肺内血管体积分布，以研究慢阻肺病程中肺血管重塑规律。方法 使用U-Net分割肺体，获取肺脏区域，减少后续血管增强与分割的运算量；借助基于多尺度Hessian矩阵的血管增强方法，获得血管的似然增强结果和轴向方向；将血管似然结果和轴向信息以数据保真项和各向异性正则项的形式融入到连续最大流分割框架，实现肺血管的自动分割。结果 在公开数据集ArteryVein和仿真数据集VascuSynth上对肺内血管分割方法的有效性进行测试；在从4家医院收集的614例临床影像数据上分析小半径血管体积占比情况，对比慢阻肺组与非慢阻肺组之间肺血管重塑差异。肺血管分割方面，对于增加不同程度的高斯噪声（σ=5，10，15，20，25，30，35）的VascuSynth仿真数据，本文方法获得的Dice值分别为0.87，0.80，0.77，0.75，0.73，0.71，0.69；对于低剂量数据集ArteryVein，Dice值为0.79。肺血管定量分析方面，非慢阻肺组和慢阻肺组的小血管体积平均占比值为0.656±0.067，0.589±0.074。不同慢阻肺分级GOLD1—4组小血管占比为0.612±0.051、0.600±0.078、0.565±0.067、0.528±0.053。结论 本文提出的肺内血管算法可以用于肺血管重塑研究，通过实验分析验证了非慢阻肺组与慢阻肺组小血管体积占比存在显著差异；基于慢阻肺分级指数（global initiative for chronic obstructive pulmonary disease，GOLD）的不同慢阻肺病人之间，小血管体积占比在轻症和重症之间也存在显著差异。

Objective Chronic obstructive pulmonary disease (COPD) is a worldwide prevalent pulmonary disease. In China, COPD is the third leading cause of death. Pulmonary function tests (PFTs) are widely used to assess COPD severity, but they cannot evaluate the contribution of each disease compartment. Pulmonary vascular remodeling is a remarkable characteristic of COPD. In the past, pulmonary vascular remodeling was regarded as an end-stage feature of COPD. However, more recent studies have found that vascular disease is present in patients with early COPD stage. Pulmonary vascular remodeling has been described as dilation of proximal vessels and pruning or narrowing of distal vessels, thereby increasing vascular resistance. The available tools for the assessment of pulmonary vascular disease remain limited. Computed tomography (CT) is the most widely used imaging modality in COPD patients; it may be utilized to assess the severity of pulmonary vascular diseases. This study aims to develop and validate an automatic method for extracting pulmonary vessels and quantifying pulmonary vascular morphology in CT images. Method The extraction of pulmonary vessels is important for automated quantitative analysis of pulmonary vascular morphology. We present an anisotropic variational approach, which incorporates appearance and orientation of pulmonary vessels as prior knowledge for extracting pulmonary vessels. The pipeline of segmentation procedure includes three stages as follows. First, because the lung segmentation can reduce the running time of subsequent stages, we apply a U-Net model, which is a convolution neural network (CNN) trained with high diversity clinical CT images to obtain the left and right lungs. Second, the response of conventional Hessian-based vesselness filters is low at the vessels' edges and bifurcations. To overcome this problem, motivated by the measurement of anisotropy of diffusion tensor, a multiscale Hessian-based vesselness filter is used to highlight pulmonary vessels and generate the axial orientation of tubular structures. This vesselness filter may mitigate the low response of branch points and maintain robust contrast of various images. Third, considering the long and thin characteristic of pulmonary vessels, we incorporate an anisotropic variational regularizer into a continuous maximal flow framework to improve the segmentation performance. This anisotropic regularizer was constructed from the orientation of pulmonary vessels in the form of matrix generated by Eigen vectors of Hessian matrix. The proposed segmentation framework was implemented with parallel computing library. For quantifying the extracted pulmonary vessels, a public clinical data set from the ArteryVein challenge and a simulated data set from the VascuSynth were used to evaluate the performance of pulmonary vessel segmentation. To verify the association between the small vessel volume and COPD, 614 patients with COPD and other pulmonary diseases were investigated with the proposed approach. Result For evaluating the pulmonary vessel segmentation method, we tested our segmentation method on simulated vessels with seven levels of Gaussian noise (σ=5,10,15,20,25,30,35) and 10 CT scans from a public clinical data set. The average dice coefficient for the simulated data set is 0.87 (σ=5), 0.80 (σ=10), 0.77 (σ=15), 0.75 (σ=20), 0.73 (σ=25), 0.71 (σ=30), and 0.69 (σ=35). The average dice coefficient for the clinical data set is 0.79. For investigating the pulmonary vessel remodeling in COPD patients, 614 CT scans from 352 patients with COPD and 262 patients with other diseases were used for quantitative analysis, where 281 cases in the COPD group contain GOLD classification information (GOLD 1:16 cases, GOLD 2:108 cases, GOLD 3:108 cases, and GOLD 4:49 cases). The average proportion of small pulmonary vessels (cross section areas <10 mm²) in the non-COPD and the COPD group was 0.656±0.067 and 0.589±0.074, respectively. The proportions of small vessels in the GOLD1-4 group were 0.612±0.051, 0.600±0.078, 0.565±0.067, and 0.528±0.053. Conclusion We proposed a pulmonary vessel segmentation method that incorporates the vessels' directions. It can be used in the study of pulmonary vascular remodeling. Experimental results have verified the difference in the proportion of small pulmonary vessel volume between the non-COPD and the COPD group, and the differences also exist in GOLD 1-4 groups.