  Accurately and precisely characterizing the morphology of small pulmonary
structures from Computed Tomography (CT) images, such as airways and vessels,
is becoming of great importance for diagnosis of pulmonary diseases. The
smaller conducting airways are the major site of increased airflow resistance
in chronic obstructive pulmonary disease (COPD), while accurately sizing
vessels can help identify arterial and venous changes in lung regions that may
determine future disorders. However, traditional methods are often limited due
to image resolution and artifacts.
  We propose a Convolutional Neural Regressor (CNR) that provides
cross-sectional measurement of airway lumen, airway wall thickness, and vessel
radius. CNR is trained with data created by a generative model of synthetic
structures which is used in combination with Simulated and Unsupervised
Generative Adversarial Network (SimGAN) to create simulated and refined airways
and vessels with known ground-truth.
  For validation, we first use synthetically generated airways and vessels
produced by the proposed generative model to compute the relative error and
directly evaluate the accuracy of CNR in comparison with traditional methods.
Then, in-vivo validation is performed by analyzing the association between the
percentage of the predicted forced expiratory volume in one second (FEV1\%) and
the value of the Pi10 parameter, two well-known measures of lung function and
airway disease, for airways. For vessels, we assess the correlation between our
estimate of the small-vessel blood volume and the lungs' diffusing capacity for
carbon monoxide (DLCO).
  The results demonstrate that Convolutional Neural Networks (CNNs) provide a
promising direction for accurately measuring vessels and airways on chest CT
images with physiological correlates.
