|Title||A machine learning approach for online automated optimization of super-resolution optical microscopy.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Durand, A, Wiesner, T, Gardner, M-A, Robitaille, L-É, Bilodeau, A, Gagné, C, De Koninck, P, Lavoie-Cardinal, F|
|Date Published||2018 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 Journal||Nat Commun|
|PubMed Central ID||PMC6286316|