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.

将人工智能集成到显微镜系统中可显著提高性能，优化图像采集和分析阶段。人工智能辅助超分辨率显微镜的开发通常受到访问大型生物数据集以及难以对异质样品进行基准测试和比较方法的限制。我们展示了逼真的受激发射耗尽显微镜仿真平台 pySTED 在开发和部署超分辨率显微镜人工智能策略方面的优势。pySTED 在受激发射耗竭显微镜中集成了经过理论和经验验证的光漂白和点扩散函数生成模型，以及模拟逼真的点扫描动力学并使用深度学习模型来复制真实图像的底层结构。该仿真环境可用于数据增强以训练深度神经网络、开发在线优化策略和训练强化学习模型。使用 pySTED 作为训练环境可以使强化学习模型弥合仿真与现实之间的差距，正如它在真实显微镜系统上的成功部署而无需微调所证明的那样。