  Airway segmentation is critical for virtual bronchoscopy and computer-aided
pulmonary disease analysis. In recent years, convolutional neural networks
(CNNs) have been widely used to delineate the bronchial tree. However, the
segmentation results of the CNN-based methods usually include many
discontinuous branches, which need manual repair in clinical use. A major
reason for the breakages is that the appearance of the airway wall can be
affected by the lung disease as well as the adjacency of the vessels, while the
network tends to overfit to these special patterns in the training set. To
learn robust features for these areas, we design a multi-branch framework that
adopts the geodesic distance transform to capture the intensity changes between
airway lumen and wall. Another reason for the breakages is the intra-class
imbalance. Since the volume of the peripheral bronchi may be much smaller than
the large branches in an input patch, the common segmentation loss is not
sensitive to the breakages among the distal branches. Therefore, in this paper,
a breakage-sensitive regularization term is designed and can be easily combined
with other loss functions. Extensive experiments are conducted on publicly
available datasets. Compared with state-of-the-art methods, our framework can
detect more branches while maintaining competitive segmentation performance.
