Photonics Group, Physics Department, Imperial College London, London, UK.
Francis Crick Institute, London, UK.
J Microsc. 2022 Nov;288(2):130-141. doi: 10.1111/jmi.13020. Epub 2021 Aug 13.
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.
我们提出了一种利用机器学习的稳健、长距离光学自动对焦系统。对于长时间采集图像数据的实验，这可能会受到因组件漂移导致的失焦影响，例如由于温度变化或机械漂移引起的失焦。它还可用于自动化载物台扫描或微孔板成像，在这些应用中，要成像的样本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的（热或机械）波动的影响，我们利用经过多日训练的卷积神经网络 (CNN) 来解决这些波动。为了解决自动对焦轴向精度和范围之间的权衡问题，我们使用具有单独 CNN 训练数据的正交光学读出方式，从而在我们的 1.3 数值孔径物镜的 600nm 景深内实现了良好的精度，在最大 +/-100μm 的失焦范围内。我们对这个自动对焦系统的性能进行了表征，并展示了它在自动化微孔板单分子定位显微镜中的应用。
