Automated segmentation of porcine airway wall layers using optical coherence tomography: comparison with manual segmentation and histology

Miranda Kirby; Anthony M. D. Lee; Tara Candido; Calum MacAulay; Pierre Lane; Stephen Lam; Harvey O. Coxson

doi:10.1117/12.2040866

4 March 2014 Automated segmentation of porcine airway wall layers using optical coherence tomography: comparison with manual segmentation and histology

Miranda Kirby, Anthony M. D. Lee, Tara Candido, Calum MacAulay, Pierre Lane, Stephen Lam, Harvey O. Coxson

Author Affiliations +

Proceedings Volume 8927, Endoscopic Microscopy IX; and Optical Techniques in Pulmonary Medicine; 89271D (2014) https://doi.org/10.1117/12.2040866
Event: SPIE BiOS, 2014, San Francisco, California, United States

Abstract

The objective was to develop an automated optical coherence tomography (OCT) segmentation method. We evaluated three ex-vivo porcine airway specimens; six non-sequential OCT images were selected from each airway specimen. Histology was also performed for each airway and histology images were co-registered to OCT images for comparison. Manual segmentation of the airway luminal area, mucosa area, submucosa area and the outer airway wall area were performed for histology and OCT images. Automated segmentation of OCT images employed a despecking filter for pre-processing, a hessian-based filter for lumen and outer airway wall area segmentation, and K-means clustering for mucosa and submucosa area segmentation. Bland-Altman analysis indicated that there was very little bias between automated OCT segmentation and histology measurements for the airway lumen area (bias=-6%, 95% CI=-21%-8%), mucosa area, (bias=-4%, 95% CI=-14%-5%), submucosa area (bias=7%, 95% CI=-7%-20%) and outer airway wall area segmentation results (bias=-5%, 95% CI=-14%-5%). We also compared automated and manual OCT segmentation and Bland-Altman analysis indicated that there was negligible bias between luminal area (bias=4%, 95% CI=1%-8%), mucosa area (bias=-3%, 95% CI=-6%-1%), submucosa area (bias=-2%, 95% CI=-10%-6%) and the outer airway wall (bias=-3%, 95% CI=-13%-6%). The automated segmentation method for OCT airway imaging developed here allows for accurate and precise segmentation of the airway wall components, suggesting that translation of this method to in vivo human airway analysis would allow for longitudinal and serial studies.

Citation Download Citation

Miranda Kirby, Anthony M. D. Lee, Tara Candido, Calum MacAulay, Pierre Lane, Stephen Lam, and Harvey O. Coxson "Automated segmentation of porcine airway wall layers using optical coherence tomography: comparison with manual segmentation and histology", Proc. SPIE 8927, Endoscopic Microscopy IX; and Optical Techniques in Pulmonary Medicine, 89271D (4 March 2014); https://doi.org/10.1117/12.2040866

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