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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
Source PublicationIEEE Photonics Technology Letters
Contribution Rank1

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
Indexed BySCI ; EI
WOS IDWOS:000516532800011
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20200208022085
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
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.
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