  Training convolutional neural networks (CNNs) for segmentation of pulmonary
airway, artery, and vein is challenging due to sparse supervisory signals
caused by the severe class imbalance between tubular targets and background. We
present a CNNs-based method for accurate airway and artery-vein segmentation in
non-contrast computed tomography. It enjoys superior sensitivity to tenuous
peripheral bronchioles, arterioles, and venules. The method first uses a
feature recalibration module to make the best use of features learned from the
neural networks. Spatial information of features is properly integrated to
retain relative priority of activated regions, which benefits the subsequent
channel-wise recalibration. Then, attention distillation module is introduced
to reinforce representation learning of tubular objects. Fine-grained details
in high-resolution attention maps are passing down from one layer to its
previous layer recursively to enrich context. Anatomy prior of lung context map
and distance transform map is designed and incorporated for better artery-vein
differentiation capacity. Extensive experiments demonstrated considerable
performance gains brought by these components. Compared with state-of-the-art
methods, our method extracted much more branches while maintaining competitive
overall segmentation performance. Codes and models are available at
http://www.pami.sjtu.edu.cn/News/56
