Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy Article Swipe
Jonathan Lightley
,
Frederik Görlitz
,
Sunil Kumar
,
Ranjan Kalita
,
Arinbjörn Kolbeinsson
,
Edwin García
,
Yuriy Alexandrov
,
Vicky Bousgouni
,
Riccardo Wysoczanski
,
Peter J. Barnes
,
Louise Donnelly
,
Chris Bakal
,
Christopher Dunsby
,
Mark A. A. Neil
,
Seth Flaxman
,
P. M. W. French
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/jmi.13020
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/jmi.13020
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/jmi.13020
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jmi.13020
- OA Status
- hybrid
- Cited By
- 21
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3165667395
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- OpenAlex ID
-
https://openalex.org/W3165667395Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/jmi.13020Digital Object Identifier
- Title
-
Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-05Full publication date if available
- Authors
-
Jonathan Lightley, Frederik Görlitz, Sunil Kumar, Ranjan Kalita, Arinbjörn Kolbeinsson, Edwin García, Yuriy Alexandrov, Vicky Bousgouni, Riccardo Wysoczanski, Peter J. Barnes, Louise Donnelly, Chris Bakal, Christopher Dunsby, Mark A. A. Neil, Seth Flaxman, P. M. W. FrenchList of authors in order
- Landing page
-
https://doi.org/10.1111/jmi.13020Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jmi.13020Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jmi.13020Direct OA link when available
- Concepts
-
Autofocus, Artificial intelligence, Microscopy, Computer science, Optics, Convolutional neural network, Data acquisition, Lens (geology), Aperture (computer memory), Computer vision, Optical microscope, Microscope, Cardinal point, Physics, Focus (optics), Acoustics, Operating system, Scanning electron microscopeTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8, 2023: 6, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2126741025, https://openalex.org/W2766376821, https://openalex.org/W2079162471, https://openalex.org/W2050202882, https://openalex.org/W3017092949, https://openalex.org/W1026382865, https://openalex.org/W2539998373, https://openalex.org/W2148172904, https://openalex.org/W6681710810, https://openalex.org/W3186572344, https://openalex.org/W2799867181, https://openalex.org/W2948499658, https://openalex.org/W2295816551, https://openalex.org/W2106316394, https://openalex.org/W2796438033, https://openalex.org/W2963163009, https://openalex.org/W2271840356, https://openalex.org/W2018333808, https://openalex.org/W6654908334, https://openalex.org/W2902016691, https://openalex.org/W2345094002, https://openalex.org/W2952246324, https://openalex.org/W2165698076, https://openalex.org/W6684207430, https://openalex.org/W2037200077, https://openalex.org/W2799626869, https://openalex.org/W1522301498 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 96, 154 |
| abstract_inverted_index.It | 46 |
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| abstract_inverted_index.We | 1, 163 |
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| abstract_inverted_index.be | 15, 27, 62, 66 |
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| abstract_inverted_index.is | 47, 102 |
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| corresponding_author_ids | https://openalex.org/A5039877686 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 16 |
| corresponding_institution_ids | https://openalex.org/I47508984 |
| citation_normalized_percentile.value | 0.88530571 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |