Enhanced u-transformer networks for automatic pulmonary vessel segmentation in ct images

Pulmonary vessel CT segmentation is important to clinical diagnosis of lung diseases. But it is still a challenge due to limited CT quality and complicated vascular structures. This paper proposes new enhanced U-transformer networks that combine transformers, a contrast enhancement block with a reverse attention block to perform end-to-end vessel segmentation. Specifically, the contrast enhancement block directly augments edge or structural information while the reverse attention block conducts the network paying more attention to blurred boundaries and uncertain regions of vessels, leading to improving the accuracy and smoothness of pulmonary vessel segmentation. We validated our proposed method on 50 CT volumes selected from LIDC-IDRI, with the experimental results demonstrating that it works more effectively and stably than currently available approaches. Particularly, the average dice similarity coefficient and recall were improved from (85.23%, 85.37%) to (86.07%, 86.67%), respectively.