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Open Access Green as soon as Postprint is submitted to ZB.
3D high resolution generative deep-learning network for fluorescence microscopy imaging.
Opt. Lett. 45, 1695-1698 (2020)
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover 20 x /1.0-NAvolume images from coarsely registered 5 x /0.16-NA volume images collected by light-sheet microscopy. This method. would provide great potential in applications which require high resolution volume imaging.
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3.714
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Mice
Language
english
Publication Year
2020
HGF-reported in Year
2020
ISSN (print) / ISBN
0146-9592
e-ISSN
1539-4794
Journal
Optics Letters
Quellenangaben
Volume: 45,
Issue: 7,
Pages: 1695-1698
Publisher
Optical Society of America (OSA)
Publishing Place
2010 Massachusetts Ave Nw, Washington, Dc 20036 Usa
Reviewing status
Peer reviewed
Institute(s)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-505800-001
WOS ID
WOS:000522794100026
Scopus ID
85082791752
PubMed ID
32235976
Erfassungsdatum
2020-04-20