  Accurate, automatic and complete extraction of pulmonary airway in medical
images plays an important role in analyzing thoracic CT volumes such as lung
cancer detection, chronic obstructive pulmonary disease (COPD), and
bronchoscopic-assisted surgery navigation. However, this task remains
challenges, due to the complex tree-like structure of the airways. In this
technical report, we use two-stage fully convolutional networks (FCNs) to
automatically segment pulmonary airway in thoracic CT scans from multi-sites.
Specifically, we firstly adopt a 3D FCN with U-shape network architecture to
segment pulmonary airway in a coarse resolution in order to accelerate medical
image analysis pipeline. And then another one 3D FCN is trained to segment
pulmonary airway in a fine resolution. In the 2022 MICCAI Multi-site
Multi-domain Airway Tree Modeling (ATM) Challenge, the reported method was
evaluated on the public training set of 300 cases and independent private
validation set of 50 cases. The resulting Dice Similarity Coefficient (DSC) is
0.914 $\pm$ 0.040, False Negative Error (FNE) is 0.079 $\pm$ 0.042, and False
Positive Error (FPE) is 0.090 $\pm$ 0.066 on independent private validation
set.

Image and Video Processing;Computer Vision and Pattern Recognition