  Automatic airway segmentation from chest computed tomography (CT) scans plays
an important role in pulmonary disease diagnosis and computer-assisted therapy.
However, low contrast at peripheral branches and complex tree-like structures
remain as two mainly challenges for airway segmentation. Recent research has
illustrated that deep learning methods perform well in segmentation tasks.
Motivated by these works, a coarse-to-fine segmentation framework is proposed
to obtain a complete airway tree. Our framework segments the overall airway and
small branches via the multi-information fusion convolution neural network
(Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous
spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it
can expend the receptive field and capture multi-scale information. Meanwhile,
boundary and location information are incorporated into semantic information.
These information are fused to help Mif-CNN utilize additional context
knowledge and useful features. To improve the performance of the segmentation
result, the CNN-based region growing method is designed to focus on obtaining
small branches. A voxel classification network (VCN), which can entirely
capture the rich information around each voxel, is applied to classify the
voxels into airway and non-airway. In addition, a shape reconstruction method
is used to refine the airway tree.
