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Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images

Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the...

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Autores principales: Zhang, Jiong, Sha, Dengfeng, Ma, Yuhui, Zhang, Dan, Tan, Tao, Xu, Xiayu, Yi, Quanyong, Zhao, Yitian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196374/
https://www.ncbi.nlm.nih.gov/pubmed/37215081
http://dx.doi.org/10.3389/fcell.2023.1181305
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author Zhang, Jiong
Sha, Dengfeng
Ma, Yuhui
Zhang, Dan
Tan, Tao
Xu, Xiayu
Yi, Quanyong
Zhao, Yitian
author_facet Zhang, Jiong
Sha, Dengfeng
Ma, Yuhui
Zhang, Dan
Tan, Tao
Xu, Xiayu
Yi, Quanyong
Zhao, Yitian
author_sort Zhang, Jiong
collection PubMed
description Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes.
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spelling pubmed-101963742023-05-20 Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images Zhang, Jiong Sha, Dengfeng Ma, Yuhui Zhang, Dan Tan, Tao Xu, Xiayu Yi, Quanyong Zhao, Yitian Front Cell Dev Biol Cell and Developmental Biology Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes. Frontiers Media S.A. 2023-05-05 /pmc/articles/PMC10196374/ /pubmed/37215081 http://dx.doi.org/10.3389/fcell.2023.1181305 Text en Copyright © 2023 Zhang, Sha, Ma, Zhang, Tan, Xu, Yi and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Zhang, Jiong
Sha, Dengfeng
Ma, Yuhui
Zhang, Dan
Tan, Tao
Xu, Xiayu
Yi, Quanyong
Zhao, Yitian
Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title_full Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title_fullStr Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title_full_unstemmed Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title_short Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
title_sort joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196374/
https://www.ncbi.nlm.nih.gov/pubmed/37215081
http://dx.doi.org/10.3389/fcell.2023.1181305
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