A machine learning approach for online automated optimization of super-resolution optical microscopy-Reference-Cited by-同舟云学术

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A machine learning approach for online automated optimization of super-resolution optical microscopy

Published:2018-12 Issue:1 Volume:9 Page:
ISSN:2041-1723
Container-title:Nature Communications
language:en
Short-container-title:Nat Commun

Author:

Durand Audrey,Wiesner Theresa,Gardner Marc-André,Robitaille Louis-Émile,Bilodeau Anthony,Gagné Christian,De Koninck Paul,Lavoie-Cardinal Flavie

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

Link

http://www.nature.com/articles/s41467-018-07668-y.pdf

Reference73 articles.

1. Huang, B., Babcock, H. & Zhuang, X. Breaking the diffraction barrier: super-resolution imaging of cells. Cell 143, 1047–1058 (2010).

2. Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. Cell Biol. 18, 685 (2017).

3. Hell, S. W. & Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 19, 780–782 (1994).

4. Grotjohann, T. et al. Diffraction-unlimited all-optical imaging and writing with a photochromic GFP. Nature 478, 204 (2011).

5. Lavoie-Cardinal, F. et al. Two-color resolft nanoscopy with green and red fluorescent photochromic proteins. Chemphyschem 15, 655–663 (2014).

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