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

    Optical Coherence Tomography
    lungs
    respiratory diseases
    tomography
    Lung
    fibrosis
    animal models
    swine
    delineation
    Pulmonary Fibrosis
    needles
    Chronic Obstructive Pulmonary Disease
    rats
    Needles
    Swine
    Animal Models
    probes
    estimates

    Cite this

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    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. {\circledC} 2013 Optical Society of America.",
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    Automated quantification of lung structures from optical coherence tomography images. / Pagnozzi, A.M.; Kirk, Rodney; Kennedy, Brendan; Sampson, David; Mclaughlin, Robert.

    In: Biomedical Optics Express, Vol. 4, No. 11, 2013, p. 2383-2395.

    Research output: Contribution to journalArticle

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    AU - Sampson, David

    AU - Mclaughlin, Robert

    PY - 2013

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