OPT OpenIR  > 瞬态光学研究室
Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning
Bai, Chen; Liu, Chao; Yu, Xianghua; Peng, Tong; Min, Junwei; Yan, Shaohui; Dan, Dan; Yao, Baoli
Department瞬态光学研究室
2019-11-15
Source PublicationIEEE Photonics Technology Letters
ISSN10411135;19410174
Volume31Issue:22Pages:1803-1806
Contribution Rank1
Abstract

The complementary beam subtraction (CBS) method can reduce the out-of-focus background and improve the axial resolution in light-sheet fluorescence microscopy (LSFM) via double scanning a Bessel and the complementary beams. With the assistance of a compressed blind deconvolution and denoising (CBDD) algorithm, the noise and blurring incurred during CBS imaging can be further removed. However, this approach requires double scanning and large computational cost. Here, we propose a deep learning-based method for LSFM, which can reconstruct high-quality images directly from the conventional Bessel beam (BB) light-sheet via a single scan. The image quality achievable with this CBS-Deep method is competitive with or better than the CBS-CBDD method, while the speed of image reconstruction is about 100 times faster. Accordingly, the proposed method can significantly improve the practicality of the CBS-CBDD system by reducing both scanning behavior and reconstruction time. The results show that this cost-effective and convenient method enables high-quality LSFM techniques to be developed and applied. © 1989-2012 IEEE.

KeywordConvolutional neural networks residual learning light-sheet fluorescence microscopy
DOI10.1109/LPT.2019.2948030
Indexed BySCI ; EI
Language英语
WOS IDWOS:000516532800011
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20200208022085
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/93183
Collection瞬态光学研究室
Corresponding AuthorYu, Xianghua
AffiliationState Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
Recommended Citation
GB/T 7714
Bai, Chen,Liu, Chao,Yu, Xianghua,et al. Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning[J]. IEEE Photonics Technology Letters,2019,31(22):1803-1806.
APA Bai, Chen.,Liu, Chao.,Yu, Xianghua.,Peng, Tong.,Min, Junwei.,...&Yao, Baoli.(2019).Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning.IEEE Photonics Technology Letters,31(22),1803-1806.
MLA Bai, Chen,et al."Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning".IEEE Photonics Technology Letters 31.22(2019):1803-1806.
Files in This Item:
File Name/Size DocType Version Access License
Imaging Enhancement (1480KB)期刊论文出版稿限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Bai, Chen]'s Articles
[Liu, Chao]'s Articles
[Yu, Xianghua]'s Articles
Baidu academic
Similar articles in Baidu academic
[Bai, Chen]'s Articles
[Liu, Chao]'s Articles
[Yu, Xianghua]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bai, Chen]'s Articles
[Liu, Chao]'s Articles
[Yu, Xianghua]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.