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

Biomedical Optics Express
David C AdamsMelissa J Suter

Abstract

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.

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Citations

Mar 8, 2019·Respirology : Official Journal of the Asian Pacific Society of Respirology·David C AdamsMelissa J Suter
Feb 26, 2019·Biomedical Optics Express·Li QiWufan Chen
Jun 9, 2019·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Lida P HaririMelissa J Suter
Apr 23, 2021·Journal of Applied Physiology·David C AdamsMelissa J Suter

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