Development of AI-assisted microscopy frameworks through realistic simulation with pySTED
Anthony Bilodeau, Albert Michaud-Gagnon, Julia Chabbert, Benoit Turcotte, Jörn Heine, Audrey Durand, Flavie Lavoie‐Cardinal
Abstract
The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning. Stimulated emission depletion microscopy is a super-resolution imaging technique that utilizes point scanning in fluorescence microscopy. pySTED is developed to aid in the development and benchmarking of optical microscopy experiments, testing it in both synthetic and real settings.