We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/–100 μm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.

中文翻译：

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鲁棒的深度学习光学自动对焦系统应用于自动化多孔板单分子定位显微镜


我们利用机器学习为显微镜提供了一种强大的远程光学自动对焦系统。这对于图像数据采集时间较长的实验非常有用，这些时间可能会受到部件漂移（例如由于温度变化或机械漂移）导致的散焦的影响。它对于自动载玻片扫描或多孔板成像也很有用，其中要成像的样品在整个图像数据采集过程中可能不在同一水平面上。为了解决光学自动对焦系统本身随时间变化（热或机械）波动的影响，我们利用经过多天训练的卷积神经网络 (CNN) 来解决此类波动。为了解决自动对焦的轴向精度和范围之间的权衡，我们使用单独的 CNN 训练数据实现正交光学读数，从而在 1.3 数值孔径物镜的 600 nm 景深范围内实现良好的精度，散焦范围为高达约 +/-100 μm。我们表征了该自动对焦系统的性能，并展示了其在自动化多孔板单分子定位显微镜中的应用。