Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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![]() ![]() ![]() | |
Source Publication | BIOMEDICAL OPTICS EXPRESS
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ISSN | 2156-7085 |
2022-03-01 | |
Volume | 13Issue:3Pages:1292-1311 |
Corresponding Author | Tian, Jie(tian@ieee.org) |
Abstract | Stripe 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 |
DOI | 10.1364/BOE.448838 |
WOS Keyword | SINGLE-CELL RESOLUTION ; EXCITATION ; REMOVAL |
Indexed By | SCI |
Language | 英语 |
Funding Project | National 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 Organization | National 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 Area | Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging |
WOS Subject | Biochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging |
WOS ID | WOS:000764828300003 |
Publisher | OPTICAL SOC AMER |
Sub direction classification | 医学影像处理与分析 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/48039 |
Collection | 中国科学院分子影像重点实验室 |
Corresponding Author | Tian, Jie |
Affiliation | 1.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 Affilication | Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
Corresponding Author Affilication | Chinese 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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