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High throughput hemogram of T cells using digital holographic microscopy and deep learning : Optics Continuum
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dc.contributor.author | Gupta, Roopam K. | |
dc.contributor.author | Hempler, Nils | |
dc.contributor.author | Malcolm, Graeme P. A. | |
dc.contributor.author | Dholakia, Kishan | |
dc.contributor.author | Powis, Simon J. | |
dc.date.accessioned | 2023-07-18T11:30:29Z | |
dc.date.available | 2023-07-18T11:30:29Z | |
dc.date.issued | 2023-03-09 | |
dc.identifier | 290716575 | |
dc.identifier | f19a7846-ad87-43e0-aae9-27a9daec54a4 | |
dc.identifier | 85165563590 | |
dc.identifier.citation | Gupta , R K , Hempler , N , Malcolm , G P A , Dholakia , K & Powis , S J 2023 , ' High throughput hemogram of T cells using digital holographic microscopy and deep learning : Optics Continuum ' , Optics Continuum , vol. 2 , no. 3 , pp. 670-682 . https://doi.org/10.1364/OPTCON.479857 | en |
dc.identifier.issn | 2770-0208 | |
dc.identifier.other | RIS: urn:4D7A6645DA44E002B4F2C54E6E25A072 | |
dc.identifier.other | ORCID: /0000-0003-4218-2984/work/139157092 | |
dc.identifier.uri | https://hdl.handle.net/10023/27983 | |
dc.description | Funding: Engineering and Physical Sciences Research Council (EP/P030017/1, EP/R004854/1); Medical Research Scotland (PhD-873-2015); Australian Research Council (FL210100099). | en |
dc.description.abstract | T cells of the adaptive immune system provide effective protection to the human body against numerous pathogenic challenges. Current labelling methods of detecting these cells, such as flow cytometry or magnetic bead labelling, are time consuming and expensive. To overcome these limitations, the label-free method of digital holographic microscopy (DHM) combined with deep learning has recently been introduced which is both time and cost effective. In this study, we demonstrate the application of digital holographic microscopy with deep learning to classify the key CD4+ and CD8+ T cell subsets. We show that combining DHM of varying fields of view, with deep learning, can potentially achieve a classification throughput rate of 78,000 cells per second with an accuracy of 76.2% for these morphologically similar cells. This throughput rate is 100 times faster than the previous studies and proves to be an effective replacement for labelling methods. | |
dc.format.extent | 13 | |
dc.format.extent | 4592362 | |
dc.language.iso | eng | |
dc.relation.ispartof | Optics Continuum | en |
dc.subject | Analytical techniques | en |
dc.subject | Diode pumped lasers | en |
dc.subject | Holographic microscopy | en |
dc.subject | Holographic techniques | en |
dc.subject | Imaging techniques | en |
dc.subject | Scanning electron microscopy | en |
dc.subject | QC Physics | en |
dc.subject | NDAS | en |
dc.subject | MCC | en |
dc.subject.lcc | QC | en |
dc.title | High throughput hemogram of T cells using digital holographic microscopy and deep learning : Optics Continuum | en |
dc.type | Journal article | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. Centre for Biophotonics | en |
dc.contributor.institution | University of St Andrews. Institute of Behavioural and Neural Sciences | en |
dc.contributor.institution | University of St Andrews. Biomedical Sciences Research Complex | en |
dc.contributor.institution | University of St Andrews. School of Physics and Astronomy | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. St Andrews Bioinformatics Unit | en |
dc.contributor.institution | University of St Andrews. Cellular Medicine Division | en |
dc.identifier.doi | 10.1364/OPTCON.479857 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | EP/P030017/1 | en |
dc.identifier.grantnumber | EP/R004854/1 | en |
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