Quantitative CT based characterization of bronchial morphology is widely used in chronic
obstructive pulmonary disease (COPD) related research and clinical studies. There
are no fully automated airway tree segmentation methods, which is critical for large
multi-site COPD studies. A critical challenge is that airway segmentation failures,
e.g., leakages or early truncation, in even a small fraction of cases warrants manual
intervention for all cases. In this paper, we present a fully automated CT-based hybrid
algorithm for human airway segmentation that combines both deep learning and conventional
image processing approaches. A three-dimensional (3-D) U-Net is developed to compute
a voxel-level likelihood map of airway lumen space from a chest CT image at total
lung capacity (TLC). This likelihood map is fed into a conventional image processing
cascade that iteratively augments airway branches and removes leakages using newly
developed freeze-and-grow and progressive threshold parameter relaxation approaches.
The new method has been applied on fifteen TLC human chest CT scans from an ongoing
COPD Study and its performance has been quantitatively compared with the results of
a semi-automated industry-standard software involving manual review and correction.
Experimental results show significant improvements in terms of branch level accuracy
using the new method as compared to the unedited results from the industry-standard
method, while matching with their manually edited results. In terms of segmentation
volume leakage, the new method significantly reduced segmentation leakages as compared
to both unedited and edited results of the industry-standard method.
