  Airway segmentation in computed tomography images can be used to analyze
pulmonary diseases, however, manual segmentation is labor intensive and relies
on expert knowledge. This manuscript details our contribution to MICCAI's 2022
Airway Tree Modelling challenge, a competition of fully automated methods for
airway segmentation. We employed a previously developed deep learning
architecture based on a modified EfficientDet (MEDSeg), training from scratch
for binary airway segmentation using the provided annotations. Our method
achieved 90.72 Dice in internal validation, 95.52 Dice on external validation,
and 93.49 Dice in the final test phase, while not being specifically designed
or tuned for airway segmentation. Open source code and a pip package for
predictions with our model and trained weights are in
https://github.com/MICLab-Unicamp/medseg.
