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Zhou, H.* ; Cai, R. ; Quan, T.* ; Liu, S.* ; Li, S.* ; Huang, Q.* ; Ertürk, A. ; Zeng, S.*

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Opt. Lett. 45, 1695-1698 (2020)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Mice
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 0146-9592
e-ISSN 1539-4794
Zeitschrift Optics Letters
Quellenangaben Band: 45, Heft: 7, Seiten: 1695-1698 Artikelnummer: , Supplement: ,
Verlag Optical Society of America (OSA)
Verlagsort 2010 Massachusetts Ave Nw, Washington, Dc 20036 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505800-001
DOI 10.1364/OL.387486
WOS ID WOS:000522794100026
Scopus ID 85082791752
PubMed ID 32235976
Erfassungsdatum 2020-04-20
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