Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography.

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.