  Pulmonary vessel segmentation is important for clinical diagnosis of
pulmonary diseases, while is also challenging due to the complicated structure.
In this work, we present an effective framework and refinement process of
pulmonary vessel segmentation from chest computed tomographic (CT) images. The
key to our approach is a 2.5D segmentation 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 the pulmonary 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
images randomly selected from LIDC dataset. Our method outperforms other
network structures by a large margin and achieves by far the highest average
DICE score of 0.9272 and precision of 0.9310, as per our knowledge from the
pulmonary vessel segmentation models available in the literature.
