Publication
Autonomous illumination control for localization microscopy
Suliana Manley, Kyle Michael Douglass, Baptiste Romain Samuel Ottino, Marcel Stefko
2018
Journal paper
2018
Journal paper
Abstract
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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