title: Automated quantification of lung structures from optical coherence tomography images.
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.
authors.affiliation: Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia.
authors.forename: Alex M
authors.initials: AM
authors.lastname: Pagnozzi
authors.forename: Rodney W
authors.initials: RW
authors.lastname: Kirk
authors.forename: Brendan F
authors.initials: BF
authors.lastname: Kennedy
authors.forename: David D
authors.initials: DD
authors.lastname: Sampson
authors.forename: Robert A
authors.initials: RA
authors.lastname: McLaughlin
keywords: (100.0100) Image processing; (100.2960) Image analysis; (110.4500) Optical coherence tomography