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

Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide r...

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Autores principales:	Audrey Durand, Theresa Wiesner, Marc-André Gardner, Louis-Émile Robitaille, Anthony Bilodeau, Christian Gagné, Paul De Koninck, Flavie Lavoie-Cardinal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/143045b6f4774232ad56311e6c9d0510
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spelling	oai:doaj.org-article:143045b6f4774232ad56311e6c9d05102021-12-02T17:33:18ZA machine learning approach for online automated optimization of super-resolution optical microscopy10.1038/s41467-018-07668-y2041-1723https://doaj.org/article/143045b6f4774232ad56311e6c9d05102018-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07668-yhttps://doaj.org/toc/2041-1723Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide range of imaging tasks.Audrey DurandTheresa WiesnerMarc-André GardnerLouis-Émile RobitailleAnthony BilodeauChristian GagnéPaul De KoninckFlavie Lavoie-CardinalNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-16 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Audrey Durand Theresa Wiesner Marc-André Gardner Louis-Émile Robitaille Anthony Bilodeau Christian Gagné Paul De Koninck Flavie Lavoie-Cardinal A machine learning approach for online automated optimization of super-resolution optical microscopy
description	Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide range of imaging tasks.
format	article
author	Audrey Durand Theresa Wiesner Marc-André Gardner Louis-Émile Robitaille Anthony Bilodeau Christian Gagné Paul De Koninck Flavie Lavoie-Cardinal
author_facet	Audrey Durand Theresa Wiesner Marc-André Gardner Louis-Émile Robitaille Anthony Bilodeau Christian Gagné Paul De Koninck Flavie Lavoie-Cardinal
author_sort	Audrey Durand
title	A machine learning approach for online automated optimization of super-resolution optical microscopy
title_short	A machine learning approach for online automated optimization of super-resolution optical microscopy
title_full	A machine learning approach for online automated optimization of super-resolution optical microscopy
title_fullStr	A machine learning approach for online automated optimization of super-resolution optical microscopy
title_full_unstemmed	A machine learning approach for online automated optimization of super-resolution optical microscopy
title_sort	machine learning approach for online automated optimization of super-resolution optical microscopy
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/143045b6f4774232ad56311e6c9d0510
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A machine learning approach for online automated optimization of super-resolution optical microscopy

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