Automated quantification of lung structures from optical coherence tomography images

A.M. Pagnozzi, Rodney Kirk, Brendan Kennedy, David Sampson, Robert Mclaughlin

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis. © 2013 Optical Society of America.
    Original languageEnglish
    Pages (from-to)2383-2395
    JournalBiomedical Optics Express
    Volume4
    Issue number11
    DOIs
    Publication statusPublished - 2013

    Fingerprint Dive into the research topics of 'Automated quantification of lung structures from optical coherence tomography images'. Together they form a unique fingerprint.

    Cite this