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

doi:10.1364/BOE.448838

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Zechen 2,3,4; Wu, Xiangjun 5; Tong, Wei 1; Zhang, Suhui 1; Yang, Xin 2,3,4; Tian, Jie 2,3,6; Hui, Hui 2,3,4
刊名	BIOMEDICAL OPTICS EXPRESS
	2022-03-01
卷号	13 期号:3 页码:1292-1311
ISSN号	2156-7085
DOI	10.1364/BOE.448838
通讯作者	Tian, Jie(tian@ieee.org)
英文摘要	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
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]
WOS关键词	SINGLE-CELL RESOLUTION ; EXCITATION ; REMOVAL
WOS研究方向	Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	OPTICAL SOC AMER
WOS记录号	WOS:000764828300003
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48038]
专题	自动化研究所_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.Peoples Liberat Army Gen Hosp, Med Ctr 6, Dept Cardiol, Beijing 100853, Peoples R China 2.CAS Key Lab Mol Imaging, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 5.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100083, Peoples R China 6.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Jinan 519000, Zhuhai, Peoples R China
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.