  Automated airway segmentation is a prerequisite for pre-operative diagnosis
and intra-operative navigation for pulmonary intervention. Due to the small
size and scattered spatial distribution of peripheral bronchi, this is hampered
by severe class imbalance between foreground and background regions, which
makes it challenging for CNN-based methods to parse distal small airways. In
this paper, we demonstrate that this problem is arisen by gradient erosion and
dilation of the neighborhood voxels. During back-propagation, if the ratio of
the foreground gradient to background gradient is small while the class
imbalance is local, the foreground gradients can be eroded by their
neighborhoods. This process cumulatively increases the noise information
included in the gradient flow from top layers to the bottom ones, limiting the
learning of small structures in CNNs. To alleviate this problem, we use group
supervision and the corresponding WingsNet to provide complementary gradient
flows to enhance the training of shallow layers. To further address the
intra-class imbalance between large and small airways, we design a General
Union loss function which obviates the impact of airway size by distance-based
weights and adaptively tunes the gradient ratio based on the learning process.
Extensive experiments on public datasets demonstrate that the proposed method
can predict the airway structures with higher accuracy and better morphological
completeness than the baselines.
