Optical super-resolution fluorescence microscopy is an essential tool in biology to visualize the sub-cellular structures with minimal invasiveness. STimulated Emission Depletion (STED) microscopy allows the nanostructures of biological samples to be investigated, even live, by routinely reaching resolutions below 60nm but is often associated with photobleaching of the fluorescent molecules. Photobleaching can be minimized by the microscopist to a certain extent by careful modulation of the imaging parameters (depletion laser power, excitation laser power, pixel dwell time, and others). This however requires knowledge of the influence of each parameter on the imaging objectives (spatial resolution, photobleaching, signal to noise ratio). More complex imaging schemes for STED microscopy, for example RESCue or DyMIN, were introduced to minimize the impact of the image acquisition on the sample but require more parameters to be carefully calibrated. We thus tackle the online optimization problem of identifying a set of optimal imaging parameters under a multi-armed bandit framework. To facilitate the quantitative validation of our machine learning-based optimization routines for super-resolution microscopy, we developed a STED simulation platform. This platform integrates most imaging parameters and photophysical properties of fluorophores for the simulation of STED microscopy experiments. Preliminary results show that our method can also be transferred to real RESCue and DyMIN experiments by optimizing the imaging parameters which alleviates the required expertise of the microscopist.

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