  With the development of medical computer-aided diagnostic systems, pulmonary
artery-vein(A/V) reconstruction 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 multitask classification network proposed can automatically learn the
differences between arteries and veins at different scales 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 reconstruction obtained by
the proposed method can provide better assistance for preoperative planning of
lung cancer surgery.
