A machine learning approach for 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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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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    author    = { Audrey Durand and Theresa Wiesner and Marc-André Gardner and Louis-Émile Robitaille and Anthony Bilodeau and Christian Gagné and Paul De Koninck and Flavie Lavoie-Cardinal },
    title     = { A machine learning approach for automated optimization of super-resolution optical microscopy },
    volume    = { 9 },
    number    = { 5247 },
    year      = { 2018 },
    month     = { 12 },
    journal   = { Nature Communications },
    web       = { }

Last modification: Oct 13 2018 9:12AM by cgagne


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