Improvement of nerve imaging speed with coherent anti-Stokes Raman scattering rigid endoscope using deep-learning noise reduction

Yamato, Naoki; Niioka, Hirohiko; Miyake, Jun; Hashimoto, Mamoru

doi:10.1038/s41598-020-72241-x


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Improvement of nerve imaging speed with coherent anti-Stokes Raman scattering rigid endoscope using deep-learning noise reduction

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/79383

Title:	Improvement of nerve imaging speed with coherent anti-Stokes Raman scattering rigid endoscope using deep-learning noise reduction
Authors:	Yamato, Naoki Browse this author
	Niioka, Hirohiko Browse this author →KAKEN DB
	Miyake, Jun Browse this author →KAKEN DB
	Hashimoto, Mamoru Browse this author →KAKEN DB
Issue Date:	16-Sep-2020
Publisher:	Nature Publishing
Journal Title:	Scientific Reports
Volume:	10
Issue:	1
Start Page:	15212
Publisher DOI:	10.1038/s41598-020-72241-x
PMID:	32938980
Abstract:	A coherent anti-Stokes Raman scattering (CARS) rigid endoscope was developed to visualize peripheral nerves without labeling for nerve-sparing endoscopic surgery. The developed CARS endoscope had a problem with low imaging speed, i.e. low imaging rate. In this study, we demonstrate that noise reduction with deep learning boosts the nerve imaging speed with CARS endoscopy. We employ fine-tuning and ensemble learning and compare deep learning models with three different architectures. In the fine-tuning strategy, deep learning models are pre-trained with CARS microscopy nerve images and retrained with CARS endoscopy nerve images to compensate for the small dataset of CARS endoscopy images. We propose using the equivalent imaging rate (EIR) as a new evaluation metric for quantitatively and directly assessing the imaging rate improvement by deep learning models. The highest EIR of the deep learning model was 7.0 images/min, which was 5 times higher than that of the raw endoscopic image of 1.4 images/min. We believe that the improvement of the nerve imaging speed will open up the possibility of reducing postoperative dysfunction by intraoperative nerve identification.
Rights:	http://creativecommons.org/licenses/by/4.0/
Type:	article
URI:	http://hdl.handle.net/2115/79383
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 橋本守

OAI-PMH ( junii2 , jpcoar_1.0 )

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