CASIA OpenIR  > 中国科学院分子影像重点实验室
Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network
Wei, Zechen1,2,3; Wu, Xiangjun4; Tong, Wei5; Zhang, Suhui5; Yang, Xin1,2,3; Tian, Jie1,2,6; Hui, Hui1,2,3
Source PublicationBIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
2022-03-01
Volume13Issue:3Pages:1292-1311
Corresponding AuthorTian, Jie(tian@ieee.org)
AbstractStripe artifacts can deteriorate the quality of light sheet fluorescence microscopy (LSFM) images. Owing to the inhomogeneous, high-absorption, or scattering objects located in the excitation light path, stripe artifacts are generated in LSFM images in various directions and types, such as horizontal, anisotropic, or multidirectional anisotropic. These artifacts severely degrade the quality of LSFM images. To address this issue, we proposed a new deep-learning based approach for the elimination of stripe artifacts. This method utilizes an encoder-decoder structure of UNet integrated with residual blocks and attention modules between successive convolutional layers. Our attention module was implemented in the residual blocks to learn useful features and suppress the residual features. The proposed network was trained and validated by generating three different degradation datasets with different types of stripe artifacts in LSFM images. Our method can effectively remove different stripes in generated and actual LSFM images distorted by stripe artifacts. Besides, quantitative analysis and extensive comparison results demonstrated that our method performs the best compared with classical image-based processing algorithms and other powerful deep-learning-based destriping methods for all three generated datasets. Thus, our method has tremendous application prospects to LSFM, and its use can be easily extended to images reconstructed by other modalities affected by the presence of stripe artifacts. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
DOI10.1364/BOE.448838
WOS KeywordSINGLE-CELL RESOLUTION ; EXCITATION ; REMOVAL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81827808] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2018167] ; Chinese Academy of Sciences Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Chinese Academy of Sciences Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City
WOS Research AreaBiochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectBiochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000764828300003
PublisherOPTICAL SOC AMER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48039
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.CAS Key Lab Mol Imaging, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
4.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100083, Peoples R China
5.Peoples Liberat Army Gen Hosp, Med Ctr 6, Dept Cardiol, Beijing 100853, Peoples R China
6.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Jinan 519000, Zhuhai, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wei, Zechen,Wu, Xiangjun,Tong, Wei,et al. Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network[J]. BIOMEDICAL OPTICS EXPRESS,2022,13(3):1292-1311.
APA Wei, Zechen.,Wu, Xiangjun.,Tong, Wei.,Zhang, Suhui.,Yang, Xin.,...&Hui, Hui.(2022).Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network.BIOMEDICAL OPTICS EXPRESS,13(3),1292-1311.
MLA Wei, Zechen,et al."Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network".BIOMEDICAL OPTICS EXPRESS 13.3(2022):1292-1311.
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