Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning | |
Bai, Chen; Liu, Chao; Yu, Xianghua; Peng, Tong; Min, Junwei![]() ![]() ![]() | |
Department | 瞬态光学研究室 |
2019-11-15 | |
Source Publication | IEEE Photonics Technology Letters
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ISSN | 10411135;19410174 |
Volume | 31Issue:22Pages:1803-1806 |
Contribution Rank | 1 |
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. |
Keyword | Convolutional neural networks residual learning light-sheet fluorescence microscopy |
DOI | 10.1109/LPT.2019.2948030 |
Indexed By | SCI ; EI |
Language | 英语 |
WOS ID | WOS:000516532800011 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
EI Accession Number | 20200208022085 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.opt.ac.cn/handle/181661/93183 |
Collection | 瞬态光学研究室 |
Corresponding Author | Yu, Xianghua |
Affiliation | State 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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Imaging Enhancement (1480KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | Application Full Text |
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