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Article: Optofluidic imaging meets deep learning: from merging to emerging

TitleOptofluidic imaging meets deep learning: from merging to emerging
Authors
Issue Date5-Jan-2024
PublisherRoyal Society of Chemistry
Citation
Lab on a Chip, 2023, v. 23, n. 5, p. 1011-1033 How to Cite?
AbstractPropelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative “smart” engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
Persistent Identifierhttp://hdl.handle.net/10722/338897
ISSN
2023 Impact Factor: 6.1
2023 SCImago Journal Rankings: 1.246
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSiu, DMD-
dc.contributor.authorLee, KCM-
dc.contributor.authorChung, BMF-
dc.contributor.authorWong, JSJ-
dc.contributor.authorZheng, G-
dc.contributor.authorTsia, KK-
dc.date.accessioned2024-03-11T10:32:20Z-
dc.date.available2024-03-11T10:32:20Z-
dc.date.issued2024-01-05-
dc.identifier.citationLab on a Chip, 2023, v. 23, n. 5, p. 1011-1033-
dc.identifier.issn1473-0197-
dc.identifier.urihttp://hdl.handle.net/10722/338897-
dc.description.abstractPropelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative “smart” engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.-
dc.languageeng-
dc.publisherRoyal Society of Chemistry-
dc.relation.ispartofLab on a Chip-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleOptofluidic imaging meets deep learning: from merging to emerging-
dc.typeArticle-
dc.identifier.doi10.1039/d2lc00813k-
dc.identifier.scopuseid_2-s2.0-85146276366-
dc.identifier.volume23-
dc.identifier.issue5-
dc.identifier.spage1011-
dc.identifier.epage1033-
dc.identifier.eissn1473-0189-
dc.identifier.isiWOS:000907545400001-
dc.identifier.issnl1473-0189-

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