  Airway segmentation is essential for chest CT image analysis. Different from
natural image segmentation, which pursues high pixel-wise accuracy, airway
segmentation focuses on topology. The task is challenging not only because of
its complex tree-like structure but also the severe pixel imbalance among
airway branches of different generations. To tackle the problems, we present a
NaviAirway method which consists of a bronchiole-sensitive loss function for
airway topology preservation and an iterative training strategy for accurate
model learning across different airway generations. To supplement the features
of airway branches learned by the model, we distill the knowledge from numerous
unlabeled chest CT images in a teacher-student manner. Experimental results
show that NaviAirway outperforms existing methods, particularly in the
identification of higher-generation bronchioles and robustness to new CT scans.
Moreover, NaviAirway is general enough to be combined with different backbone
models to significantly improve their performance. NaviAirway can generate an
airway roadmap for Navigation Bronchoscopy and can also be applied to other
scenarios when segmenting fine and long tubular structures in biomedical
images. The code is publicly available on
https://github.com/AntonotnaWang/NaviAirway.
