Open AccessAutonomous illumination control for localization microscopy
Author(s) -
Marcel Štefko,
Baptiste Ottino,
Kyle M. Douglass,
Suliana Manley
Publication year - 2018
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.030882
Subject(s) - microscopy , computer science , optics , microscope , offset (computer science) , a priori and a posteriori , automation , super resolution microscopy , artificial intelligence , computer vision , light sheet fluorescence microscopy , image processing , image resolution , physics , scanning confocal electron microscopy , image (mathematics) , mechanical engineering , philosophy , epistemology , programming language , engineering
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.