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

TitleA machine learning approach for online automated optimization of super-resolution optical microscopy.
Publication TypeJournal Article
Year of Publication2018
AuthorsDurand A, Wiesner T, Gardner M-A, Robitaille L-É, Bilodeau A, Gagné C, De Koninck P, Lavoie-Cardinal F
JournalNat Commun
Date Published2018 12 07

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.

Alternate JournalNat Commun
PubMed ID30531817
PubMed Central IDPMC6286316


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Canadian Institutes of Health Research - Instituts de recherche en santé du Canada Fonds de recherche du Québec – Nature et technologies (FRQNT)Fonds de la recherche en santé du Québec   Natural Sciences and Engineering Research Council of Canada (NSERC) - Conseil de recherche en sciences naturelles et en génie du Canada (CRSNG)innovation.caHuman Frontier Science ProgramCanada First Research Excellence FundSentinelle Nord