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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
作者部门瞬态光学研究室
2019-11-15
发表期刊IEEE Photonics Technology Letters
ISSN10411135;19410174
卷号31期号:22页码:1803-1806
产权排序1
摘要

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.

关键词Convolutional neural networks residual learning light-sheet fluorescence microscopy
DOI10.1109/LPT.2019.2948030
收录类别SCI ; EI
语种英语
WOS记录号WOS:000516532800011
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20200208022085
引用统计
被引频次:16[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/93183
专题瞬态光学研究室
通讯作者Yu, Xianghua
作者单位State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
推荐引用方式
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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