  Airway segmentation, especially bronchioles segmentation, is an important but
challenging task because distal bronchus are sparsely distributed and of a fine
scale. Existing neural networks usually exploit sparse topology to learn the
connectivity of bronchioles and inefficient shallow features to capture such
high-frequency information, leading to the breakage or missed detection of
individual thin branches. To address these problems, we contribute a new
bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that
emphasizes fine-scale bronchioles segmentation in a simple-but-effective
manner. First, Deep Dense Supervision (DDS) is proposed by constructing local
dense topology skillfully and implementing dense topological learning on a
specific shallow feature layer. GDDS further empowers the shallow features with
better perception ability to detect bronchioles, even the ones that are not
easily discernible to the naked eye. Extensive experiments on the BAS benchmark
dataset have shown that our method promotes the network to have a high
sensitivity in capturing fine-scale branches and outperforms state-of-the-art
methods by a large margin (+12.8 % in BD and +8.8 % in TD) while only
introducing a small number of extra parameters.
