  With the development of medical computer-aided diagnostic systems, pulmonary
artery-vein(A/V) separation plays a crucial role in assisting doctors in
preoperative planning for lung cancer surgery. However, distinguishing arterial
from venous irrigation in chest CT images remains a challenge due to the
similarity and complex structure of the arteries and veins. We propose a novel
method for automatic separation of pulmonary arteries and veins from chest CT
images. The method consists of three parts. First, global connection
information and local feature information are used to construct a complete
topological tree and ensure the continuity of vessel reconstruction. Second,
the Twin-Pipe network proposed can automatically learn the differences between
arteries and veins at different levels to reduce classification errors caused
by changes in terminal vessel characteristics. Finally, the topology optimizer
considers interbranch and intrabranch topological relationships to maintain
spatial consistency to avoid the misclassification of A/V irrigations. We
validate the performance of the method on chest CT images. Compared with manual
classification, the proposed method achieves an average accuracy of 96.2% on
noncontrast chest CT. In addition, the method has been proven to have good
generalization, that is, the accuracies of 93.8% and 94.8% are obtained for CT
scans from other devices and other modes, respectively. The result of pulmonary
artery-vein obtained by the proposed method can provide better assistance for
preoperative planning of lung cancer surgery.
