  Segmentation of the airway tree from chest computed tomography (CT) images is
critical for quantitative assessment of airway diseases including
bronchiectasis and chronic obstructive pulmonary disease (COPD). However,
obtaining an accurate segmentation of airways from CT scans is difficult due to
the high complexity of airway structures. Recently, deep convolutional neural
networks (CNNs) have become the state-of-the-art for many segmentation tasks,
and in particular the so-called Unet architecture for biomedical images.
However, its application to the segmentation of airways still remains a
challenging task. This work presents a simple but robust approach based on a 3D
Unet to perform segmentation of airways from chest CTs. The method is trained
on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the
influence of different loss functions and data augmentation techniques, and
reach an average dice coefficient of 0.8 between the ground-truth and our
automated segmentations.
