Automated segmentation of lung airway wall area measurements from bronchoscopic optical coherence tomography imaging

Mohammadreza Heydarian; Stephen Choy; Andrew Wheatley; David McCormack; Harvey O. Coxson; Stephen Lam; Grace Parraga

doi:10.1117/12.878194

9 March 2011 Automated segmentation of lung airway wall area measurements from bronchoscopic optical coherence tomography imaging

Mohammadreza Heydarian, Stephen Choy, Andrew Wheatley, David McCormack, Harvey O. Coxson, Stephen Lam, Grace Parraga

Author Affiliations +

Proceedings Volume 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging; 79651M (2011) https://doi.org/10.1117/12.878194
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Chronic Obstructive Pulmonary Disease (COPD) affects almost 600 million people and is currently the fourth leading cause of death worldwide. COPD is an umbrella term for respiratory symptoms that accompany destruction of the lung parenchyma and/or remodeling of the airway wall, the sum of which result in decreased expiratory flow, dyspnea and gas trapping. Currently, x-ray computed tomography (CT) is the main clinical method used for COPD imaging, providing excellent spatial resolution for quantitative tissue measurements although dose limitations and the fundamental spatial resolution of CT limit the measurement of airway dimensions beyond the 5^th generation. To address this limitation, we are piloting the use of bronchoscopic Optical Coherence Tomography (OCT), by exploiting its superior spatial resolution of 5-15 micrometers for in vivo airway imaging. Currently, only manual segmentation of OCT airway lumen and wall have been reported but manual methods are time consuming and prone to observer variability. To expand the utility of bronchoscopic OCT, automatic and robust measurement methods are required. Therefore, our objective was to develop a fully automated method for segmenting OCT airway wall dimensions and here we explore several different methods of image-regeneration, voxel clustering and post-processing. Our resultant automated method used K-means or Fuzzy c-means to cluster pixel intensity and then a series of algorithms (i.e. cluster selection, artifact removal, de-noising) was applied to process the clustering results and segment airway wall dimensions. This approach provides a way to automatically and rapidly segment and reproducibly measure airway lumen and wall area.

Citation Download Citation

Mohammadreza Heydarian, Stephen Choy, Andrew Wheatley, David McCormack, Harvey O. Coxson, Stephen Lam, and Grace Parraga "Automated segmentation of lung airway wall area measurements from bronchoscopic optical coherence tomography imaging", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79651M (9 March 2011); https://doi.org/10.1117/12.878194

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