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17 March 2023 Deep learning accelerated quantitative assessment for optical coherence tomography images of acute chlorine gas inhalation injury in a rabbit model
Zhikai Zhu, Hongqiu Lei, Raksha Sreeramachandra Murthy, Theodore V. Nguyen, Katelyn K. Dilley, Donggyoon Hong, Xiao Gao, Matthew Brenner, Zhongping Chen
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Proceedings Volume 12354, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2023; 123540M (2023) https://doi.org/10.1117/12.2668720
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
In this study, Optical Coherence Tomography (OCT) was used to image the large upper airway in a rabbit model. U-net convolutional neural network (CNN) was used to automate the segmentation of large airway edema and tissue changes. Peak edema volume was reached at 30-minutes post-chlorine gas exposure, then down trended until the 6-hour timepoint. Herein, we show the streamlining of OCT imaging analysis status-post chlorine inhalation injury using CNNs.
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Zhikai Zhu, Hongqiu Lei, Raksha Sreeramachandra Murthy, Theodore V. Nguyen, Katelyn K. Dilley, Donggyoon Hong, Xiao Gao, Matthew Brenner, and Zhongping Chen "Deep learning accelerated quantitative assessment for optical coherence tomography images of acute chlorine gas inhalation injury in a rabbit model", Proc. SPIE 12354, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2023, 123540M (17 March 2023); https://doi.org/10.1117/12.2668720
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KEYWORDS
Chlorine gas
Optical coherence tomography
Deep learning
Injuries
Chlorine
In vivo imaging
Nervous system
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