Journal
OPTICS EXPRESS
Volume 26, Issue 23, Pages 30882-30900Publisher
OPTICAL SOC AMER
DOI: 10.1364/OE.26.030882
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Funding
- EPFL
- SystemsX.ch Transition Postdoc Fellowship [2014/227]
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Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning-based molecule density estimator that is accurate over an extended range of densities. This system obviates the need to fine-tune parameters and enables robust, autonomous illumination control for localization microscopy. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
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