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- Publisher Website: 10.1364/OL.441743
- Scopus: eid_2-s2.0-85122086509
- PMID: 34913909
- WOS: WOS:000730864500004
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Article: Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing
Title | Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing |
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Authors | |
Issue Date | 2021 |
Citation | Optics Letters, 2021, v. 46, n. 24, p. 6023-6026 How to Cite? |
Abstract | Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges. |
Persistent Identifier | http://hdl.handle.net/10722/315378 |
ISSN | 2023 Impact Factor: 3.1 2023 SCImago Journal Rankings: 1.040 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ikoma, Hayato | - |
dc.contributor.author | Kudo, Takamasa | - |
dc.contributor.author | Peng, Yifan | - |
dc.contributor.author | Broxton, Michael | - |
dc.contributor.author | Wetzstein, Gordon | - |
dc.date.accessioned | 2022-08-05T10:18:40Z | - |
dc.date.available | 2022-08-05T10:18:40Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Optics Letters, 2021, v. 46, n. 24, p. 6023-6026 | - |
dc.identifier.issn | 0146-9592 | - |
dc.identifier.uri | http://hdl.handle.net/10722/315378 | - |
dc.description.abstract | Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges. | - |
dc.language | eng | - |
dc.relation.ispartof | Optics Letters | - |
dc.title | Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1364/OL.441743 | - |
dc.identifier.pmid | 34913909 | - |
dc.identifier.scopus | eid_2-s2.0-85122086509 | - |
dc.identifier.volume | 46 | - |
dc.identifier.issue | 24 | - |
dc.identifier.spage | 6023 | - |
dc.identifier.epage | 6026 | - |
dc.identifier.eissn | 1539-4794 | - |
dc.identifier.isi | WOS:000730864500004 | - |