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Nat Commun. 2018 Dec 7;9(1):5247. doi: 10.1038/s41467-018-07668-y.

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

Author information

1
Département de génie électrique et de génie informatique, Université Laval, Québec, QC, G1V 0A6, Canada. audrey.durand@mcgill.ca.
2
CERVO Brain Research Center, 2601 de la Canardière, Québec, QC, G1J 2G3, Canada.
3
Département de génie électrique et de génie informatique, Université Laval, Québec, QC, G1V 0A6, Canada.
4
Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada.
5
CERVO Brain Research Center, 2601 de la Canardière, Québec, QC, G1J 2G3, Canada. flavie.lavoie-cardinal.1@ulaval.ca.

Abstract

Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.

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