  This paper presents a fully automatic and end-to-end optimised airway
segmentation method for thoracic computed tomography, based on the U-Net
architecture. We use a simple and low-memory 3D U-Net as backbone, which allows
the method to process large 3D image patches, often comprising full lungs, in a
single pass through the network. This makes the method simple, robust and
efficient. We validated the proposed method on three datasets with very
different characteristics and various airway abnormalities: i) a dataset of
pediatric patients including subjects with cystic fibrosis, ii) a subset of the
Danish Lung Cancer Screening Trial, including subjects with chronic obstructive
pulmonary disease, and iii) the EXACT'09 public dataset. We compared our method
with other state-of-the-art airway segmentation methods, including relevant
learning-based methods in the literature evaluated on the EXACT'09 data. We
show that our method can extract highly complete airway trees with few false
positive errors, on scans from both healthy and diseased subjects, and also
that the method generalizes well across different datasets. On the EXACT'09
test set, our method achieved the second highest sensitivity score among all
methods that reported good specificity.
