Pulmonary vessel segmentation is important for clinical di- agnosis of pulmonary diseases, while is also challenging due to the com- plicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel segmentation from chest com- puted tomographic (CT) images. The key to our approach is a 2.5D seg- mentation network applied from three orthogonal axes, which presents a robust and fully automated pulmonary vessel segmentation result with lower network complexity and memory usage compared to 3D networks. The slice radius is introduced to convolve the adjacent information of the center slice and the multi-planar fusion optimizes the presentation of intra and inter slice features. Besides, the tree-like structure of pul- monary vessel is extracted in the post-processing process, which is used for segmentation refining and pruning. In the evaluation experiments, three fusion methods are tested and the most promising one is compared with the state-of-the-art 2D and 3D structures on 300 cases of lung im- ages randomly selected from LIDC dataset. Our method outperforms other network structures by a large margin and achieves by far the high- est average DICE score of 0.9272 and a precision of 0.9310, as per our knowledge from the pulmonary vessel segmentation models available in literature.