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CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images

BACKGROUND: The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomogr...

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Autores principales: Chen, Wen, Yu, Xiangle, Ye, Yiru, Gao, Hebei, Cao, Xinyuan, Lin, Guangqing, Zhang, Riyan, Li, Zixuan, Wang, Xinmin, Zhou, Yuheng, Shen, Meixiao, Shao, Yilei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017933/
https://www.ncbi.nlm.nih.gov/pubmed/36938499
http://dx.doi.org/10.1177/20406223231159616
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author Chen, Wen
Yu, Xiangle
Ye, Yiru
Gao, Hebei
Cao, Xinyuan
Lin, Guangqing
Zhang, Riyan
Li, Zixuan
Wang, Xinmin
Zhou, Yuheng
Shen, Meixiao
Shao, Yilei
author_facet Chen, Wen
Yu, Xiangle
Ye, Yiru
Gao, Hebei
Cao, Xinyuan
Lin, Guangqing
Zhang, Riyan
Li, Zixuan
Wang, Xinmin
Zhou, Yuheng
Shen, Meixiao
Shao, Yilei
author_sort Chen, Wen
collection PubMed
description BACKGROUND: The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomography (OCT) has been frequently used to image the ciliary muscle and its changes during accommodation in vivo. However, the segmentation process is cumbersome and time-consuming due to the large image data sets and the impact of low imaging quality. OBJECTIVES: This study aimed to establish a fully automatic method for segmenting and quantifying the ciliary muscle on the basis of optical coherence tomography (OCT) images. DESIGN: A perspective cross-sectional study. METHODS: In this study, 3500 signed images were used to develop a deep learning system. A novel deep learning algorithm was created from the widely used U-net and a full-resolution residual network to realize automatic segmentation and quantification of the ciliary muscle. Finally, the algorithm-predicted results and manual annotation were compared. RESULTS: For segmentation performed by the system, the total mean pixel value difference (PVD) was 1.12, and the Dice coefficient, intersection over union (IoU), and sensitivity values were 93.8%, 88.7%, and 93.9%, respectively. The performance of the system was comparable with that of experienced specialists. The system could also successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. CONCLUSION: We developed an automatic segmentation framework for the ciliary muscle that can be used to analyze the morphological parameters of the ciliary muscle and its dynamic changes during accommodation.
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spelling pubmed-100179332023-03-17 CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images Chen, Wen Yu, Xiangle Ye, Yiru Gao, Hebei Cao, Xinyuan Lin, Guangqing Zhang, Riyan Li, Zixuan Wang, Xinmin Zhou, Yuheng Shen, Meixiao Shao, Yilei Ther Adv Chronic Dis Artificial Intelligence related Optical Bioimaging markers in inflammatory and degenerative diseases BACKGROUND: The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomography (OCT) has been frequently used to image the ciliary muscle and its changes during accommodation in vivo. However, the segmentation process is cumbersome and time-consuming due to the large image data sets and the impact of low imaging quality. OBJECTIVES: This study aimed to establish a fully automatic method for segmenting and quantifying the ciliary muscle on the basis of optical coherence tomography (OCT) images. DESIGN: A perspective cross-sectional study. METHODS: In this study, 3500 signed images were used to develop a deep learning system. A novel deep learning algorithm was created from the widely used U-net and a full-resolution residual network to realize automatic segmentation and quantification of the ciliary muscle. Finally, the algorithm-predicted results and manual annotation were compared. RESULTS: For segmentation performed by the system, the total mean pixel value difference (PVD) was 1.12, and the Dice coefficient, intersection over union (IoU), and sensitivity values were 93.8%, 88.7%, and 93.9%, respectively. The performance of the system was comparable with that of experienced specialists. The system could also successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. CONCLUSION: We developed an automatic segmentation framework for the ciliary muscle that can be used to analyze the morphological parameters of the ciliary muscle and its dynamic changes during accommodation. SAGE Publications 2023-03-14 /pmc/articles/PMC10017933/ /pubmed/36938499 http://dx.doi.org/10.1177/20406223231159616 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Artificial Intelligence related Optical Bioimaging markers in inflammatory and degenerative diseases
Chen, Wen
Yu, Xiangle
Ye, Yiru
Gao, Hebei
Cao, Xinyuan
Lin, Guangqing
Zhang, Riyan
Li, Zixuan
Wang, Xinmin
Zhou, Yuheng
Shen, Meixiao
Shao, Yilei
CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title_full CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title_fullStr CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title_full_unstemmed CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title_short CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
title_sort cms-net: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images
topic Artificial Intelligence related Optical Bioimaging markers in inflammatory and degenerative diseases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017933/
https://www.ncbi.nlm.nih.gov/pubmed/36938499
http://dx.doi.org/10.1177/20406223231159616
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