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Autonomous illumination control for localization microscopy
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DOI：10.1364/OE.26.030882.png)
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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.
Keyword:
SUPERRESOLUTION
RESOLUTION
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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