Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.
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ICR Authors
Authors
Lightley, J
Görlitz, F
Kumar, S
Kalita, R
Kolbeinsson, A
Garcia, E
Alexandrov, Y
Bousgouni, V
Wysoczanski, R
Barnes, P
Donnelly, L
Bakal, C
Dunsby, C
Neil, MAA
Flaxman, S
French, PMW
Görlitz, F
Kumar, S
Kalita, R
Kolbeinsson, A
Garcia, E
Alexandrov, Y
Bousgouni, V
Wysoczanski, R
Barnes, P
Donnelly, L
Bakal, C
Dunsby, C
Neil, MAA
Flaxman, S
French, PMW
Document Type
Journal Article
Date
2021-06-04
Date Accepted
2021-05-05
Abstract
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.
Citation
Journal of microscopy, 2021
Source Title
Publisher
WILEY
ISSN
0022-2720
eISSN
1365-2818
Collections
Research Team
Dynamical Cell Systems
Dynamical Cell Systems
Dynamical Cell Systems