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Full-color optically-sectioned imaging by wide-field microscopy via deep-learning
Bai, Chen1; Qian, Jia1,2; Dang, Shipei1,2; Peng, Tong1; Min, Junwei1; Lei, Ming1; Dan, Dan1; Yao, Baoli1
Department瞬态光学研究室
2020-05-01
Source PublicationBIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
Volume11Issue:5Pages:2619-2632
Contribution Rank1
Abstract

Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM, i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an extended optical sectioning capability directly from the FC-WFM z-stack data. Case studies of different specimens with a specific imaging system are used to illustrate this method. Consequently, the image quality achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of 3D information and spatial resolution, while the reconstruction data size is 21-fold smaller and the in-focus depth is doubled. This technique significantly reduces the 3D data acquisition requirements without losing detail and improves the 3D imaging speed by extracting the optical sectioning in the depth-of-field. This cost-effective and convenient method offers a promising tool to observe high-precision color 3D spatial distributions of biological samples. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

DOI10.1364/BOE.389852
Indexed BySCI ; EI
Language英语
WOS IDWOS:000532568000024
PublisherOPTICAL SOC AMER
EI Accession Number20202008666797
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/93449
Collection瞬态光学研究室
Corresponding AuthorDan, Dan
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Bai, Chen,Qian, Jia,Dang, Shipei,et al. Full-color optically-sectioned imaging by wide-field microscopy via deep-learning[J]. BIOMEDICAL OPTICS EXPRESS,2020,11(5):2619-2632.
APA Bai, Chen.,Qian, Jia.,Dang, Shipei.,Peng, Tong.,Min, Junwei.,...&Yao, Baoli.(2020).Full-color optically-sectioned imaging by wide-field microscopy via deep-learning.BIOMEDICAL OPTICS EXPRESS,11(5),2619-2632.
MLA Bai, Chen,et al."Full-color optically-sectioned imaging by wide-field microscopy via deep-learning".BIOMEDICAL OPTICS EXPRESS 11.5(2020):2619-2632.
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