Robust optical autofocus system utilizing neural networks trained for extended range and time-course and automated multiwell plate imaging including single molecule localization microscopy
Robust optical autofocus system utilizing neural networks trained for extended range and time-course and automated multiwell plate imaging including single molecule localization microscopy
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DOI：
10.1101/2021.03.05.431171
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发表时间：
2021-03
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通讯作者：
J. Lightley;F. Görlitz;S. Kumar;R. Kalita;A. Kolbeinsson;E. Garcia;Y. Alexandrov;V. Bousgouni;R. Wysoczanski;P. Barnes;L. Donnelly;C. Bakal;C. Dunsby;M. Neil;S. Flaxman;P. French
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作者：
J. Lightley;F. Görlitz;S. Kumar;R. Kalita;A. Kolbeinsson;E. Garcia;Y. Alexandrov;V. Bousgouni;R. Wysoczanski;P. Barnes;L. Donnelly;C. Bakal;C. Dunsby;M. Neil;S. Flaxman;P. French
我们提出了一个稳健的，利用机器学习的显微镜远距离光学自动聚焦系统。这对于长图像数据采集时间的实验是有用的，该长图像数据采集时间可能受到组件漂移(例如，由于温度变化或机械漂移)引起的散焦的影响。这对于自动载玻片扫描或多孔板成像也很有用，在这些情况下，要成像的样本(S)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中随时间变化的(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)来解释这种波动。为了解决轴向精度和自动对焦范围之间的权衡，我们使用单独的cnn训练数据实现了正交光学读数，从而在高达约+/−100μm的离焦范围内实现了1.3型数值孔径物镜的600 nm景深内的精度。
We present a 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, e.g. 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 utilise 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 characterise the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localisation microscopy.