Open AccessAutomated segmentation and quantification of airway mucus with endobronchial optical coherence tomography
Author(s) -
David C. Adams,
Hamid Pahlevaninezhad,
Margit V. Szabari,
Josalyn L. Cho,
Daniel L. Hamilos,
Mehmet Kesımer,
Richard C. Boucher,
Andrew D. Luster,
Benjamin D. Medoff,
Melissa J. Suter
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.004729
Subject(s) - optical coherence tomography , mucus , airway , segmentation , computer science , optical tomography , biomedical engineering , artificial intelligence , pathology , medicine , radiology , biology , surgery , ecology
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.