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

Audrey Durand (), Theresa Wiesner, Marc-André Gardner, Louis-Émile Robitaille, Anthony Bilodeau, Christian Gagné, Paul De Koninck and Flavie Lavoie-Cardinal ()
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Audrey Durand: Université Laval
Theresa Wiesner: CERVO Brain Research Center
Marc-André Gardner: Université Laval
Louis-Émile Robitaille: Université Laval
Anthony Bilodeau: CERVO Brain Research Center
Christian Gagné: Université Laval
Paul De Koninck: CERVO Brain Research Center
Flavie Lavoie-Cardinal: CERVO Brain Research Center

Nature Communications, 2018, vol. 9, issue 1, 1-16

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-07668-y

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