PT  - JOURNAL ARTICLE
AU  - Marcel Štefko
AU  - Baptiste Ottino
AU  - Kyle M. Douglass
AU  - Suliana Manley
TI  - Design Principles for Autonomous Illumination Control in Localization Microscopy
AID  - 10.1101/295519
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 295519
4099  - http://biorxiv.org/content/early/2018/04/05/295519.short
4100  - http://biorxiv.org/content/early/2018/04/05/295519.full
AB  - 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 molecule density estimator that is accurate over an extended range. This system obviates the need to fine-tune parameters and demonstrates the design of modular illumination control for localization microscopy.