Subglottic stenosis is a severe and challenging disease to manage in neonates. Previous reports describe the usage of long-range optical coherence tomography (LR-OCT) to image the subglottis through an endotracheal tube and potentially identify subepithelial changes in subglottic mucosa which are correlated with edema or scar tissue. A major challenge associated with OCT imaging is that large volumes of data (1-2 GB) are acquired with each airway scan, with no existing automated method for image analysis and tissue measurement. We have developed an innovative MATLAB based auto-segmentation program which identifies and measures tissue layers within the mucosa. LR-OCT data sets of 21 neonates were analyzed for mucosal thickness of the proximal trachea, subglottis and larynx. The auto-segmentation measurements were compared with measurements from manual tracings by a single operator. We found statistically significant associations between the thickness of the mucosa (p<0.001) and submucosa (p<0.001) layers in the upper airway when comparing these two segmentation processes. The auto-segmentation program segmented the OCT images on average over 8 times faster than the manual segmentation software. Following auto-segmentation, OCT images were also analyzed for texture analysis properties using ANOVA. Automated segmentation and measurement of OCT data sets is an efficient and precise method to analyze large volume LR-OCT data stacks. This may ultimately help provide vital objective information about the airway in real-time, which would aid clinicians in making management decisions for intubated neonates.
Konrad Kozlowski, Giriraj Sharma, Brian Wong, Jason Chen, Zhongping Chen, Joseph Jing, and Li Qi, "Clinical evaluation of subglottic stenosis in neonates using automatic segmentation of optical coherence tomography via dynamic programming (Conference Presentation)," Proc. SPIE 10469, Optical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018, 104690B (Presented at SPIE BiOS: January 27, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2299294.5751463156001.
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