Coordinate-based neural representations for computational adaptive optics in widefield microscopy Article Swipe
Iksung Kang
,
Qinrong Zhang
,
Stella X. Yu
,
Na Ji
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.03812
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.03812
Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here, we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single input 3D image stack without the need for external training dataset. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA's performance. Using CoCoA, we demonstrated the first in vivo widefield mouse brain imaging using machine-learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.03812
- https://arxiv.org/pdf/2307.03812
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383987281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383987281Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.03812Digital Object Identifier
- Title
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Coordinate-based neural representations for computational adaptive optics in widefield microscopyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-07Full publication date if available
- Authors
-
Iksung Kang, Qinrong Zhang, Stella X. Yu, Na JiList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.03812Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.03812Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.03812Direct OA link when available
- Concepts
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Wavefront, Adaptive optics, Computer science, Optics, Distortion (music), Image quality, Computer vision, Artificial intelligence, Artificial neural network, Microscopy, Deformable mirror, Physics, Image (mathematics), Bandwidth (computing), Amplifier, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.validated | 94 |
| abstract_inverted_index.wavefront | 32, 40, 62 |
| abstract_inverted_index.widefield | 87, 122 |
| abstract_inverted_index.algorithm, | 57 |
| abstract_inverted_index.applicable | 146 |
| abstract_inverted_index.biological | 9 |
| abstract_inverted_index.complexity | 47 |
| abstract_inverted_index.corrective | 43 |
| abstract_inverted_index.distortion | 33 |
| abstract_inverted_index.estimation | 63 |
| abstract_inverted_index.extraction | 68 |
| abstract_inverted_index.increasing | 45 |
| abstract_inverted_index.microscopy | 1, 148 |
| abstract_inverted_index.modalities | 149 |
| abstract_inverted_index.resolution | 37 |
| abstract_inverted_index.structural | 66 |
| abstract_inverted_index.structures | 10 |
| abstract_inverted_index.aberration. | 27 |
| abstract_inverted_index.implemented | 84 |
| abstract_inverted_index.information | 67 |
| abstract_inverted_index.performance | 96 |
| abstract_inverted_index.resolution. | 13 |
| abstract_inverted_index.subcellular | 12 |
| abstract_inverted_index.Importantly, | 101 |
| abstract_inverted_index.demonstrated | 117 |
| abstract_inverted_index.non-invasive | 6 |
| abstract_inverted_index.performance. | 113 |
| abstract_inverted_index.Incorporating | 130 |
| abstract_inverted_index.characterized | 107 |
| abstract_inverted_index.quantitatively | 106 |
| abstract_inverted_index.sample-induced | 25 |
| abstract_inverted_index.systematically | 103 |
| abstract_inverted_index.representations | 133 |
| abstract_inverted_index.self-supervised | 54, 140 |
| abstract_inverted_index.coordinate-based | 131 |
| abstract_inverted_index.three-dimensional | 65 |
| abstract_inverted_index.diffraction-limited | 36 |
| abstract_inverted_index.machine-learning-based | 127 |
| abstract_inverted_index.direct-wavefront-sensing-based | 98 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.76106487 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |