Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network

doi:10.1364/BOE.448838

	Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network
	Wei, Zechen 1,2,3; Wu, Xiangjun 4; Tong, Wei 5; Zhang, Suhui 5; Yang, Xin1,2,3 ; Tian, Jie 1,2,6; Hui, Hui1,2,3
发表期刊	BIOMEDICAL OPTICS EXPRESS
ISSN	2156-7085
	2022-03-01
卷号	13 期号:3 页码:1292-1311
通讯作者	Tian, Jie(tian@ieee.org)
摘要	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
关键词[WOS]	SINGLE-CELL RESOLUTION ; EXCITATION ; REMOVAL
收录类别	SCI
语种	英语
资助项目	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]
项目资助者	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研究方向	Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Biochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000764828300003
出版者	OPTICAL SOC AMER
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48039
专题	中国科学院分子影像重点实验室
通讯作者	Tian, Jie
作者单位	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
第一作者单位	中国科学院分子影像重点实验室
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 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.