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User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Keondo | - |
dc.contributor.author | Kim, Seong-Eun | - |
dc.contributor.author | Doh, Junsang | - |
dc.contributor.author | Kim, Keehoon | - |
dc.contributor.author | Chung, Wan Kyun | - |
dc.date.accessioned | 2024-05-16T01:21:46Z | - |
dc.date.available | 2024-05-16T01:21:46Z | - |
dc.date.created | 2021-06-03 | - |
dc.date.created | 2021-06-03 | - |
dc.date.issued | 2021-05 | - |
dc.identifier.citation | Lab on a Chip - Miniaturisation for Chemistry and Biology, Vol.21 No.9, pp.1798-1810 | - |
dc.identifier.issn | 1473-0197 | - |
dc.identifier.uri | https://hdl.handle.net/10371/202461 | - |
dc.description.abstract | Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enable the discrimination of cell-to-cell differences in image-activated cell sorters. However, such systems are challenging to implement from a technical perspective due to the advanced imaging and sorting requirements and the long processing times of deep learning algorithms. Here, we introduce a user-friendly image-activated microfluidic sorting technique based on a fast deep learning model under the TensorRT framework to enable sorting decisions within 3 ms. The proposed sorter employs a significantly simplified operational procedure based on the use of a syringe connected to a piezoelectric actuator. The sorter has a 2.5 ms latency. The utility of the sorter was demonstrated through real-time sorting of fluorescent polystyrene beads and cells. The sorter achieved 98.0%, 95.1%, and 94.2% sorting purities for 15 mu m and 10 mu m beads, HL-60 and Jurkat cells, and HL-60 and K562 cells, respectively, with a throughput of up to 82.8 events per second (eps). | - |
dc.language | 영어 | - |
dc.publisher | Royal Society of Chemistry | - |
dc.title | User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm | - |
dc.type | Article | - |
dc.identifier.doi | 10.1039/d0lc00747a | - |
dc.citation.journaltitle | Lab on a Chip - Miniaturisation for Chemistry and Biology | - |
dc.identifier.wosid | 000646819400010 | - |
dc.identifier.scopusid | 2-s2.0-85105332868 | - |
dc.citation.endpage | 1810 | - |
dc.citation.number | 9 | - |
dc.citation.startpage | 1798 | - |
dc.citation.volume | 21 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Doh, Junsang | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | SINGLE-CELL | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | FLOW-CYTOMETRY | - |
dc.subject.keywordPlus | CHIP | - |
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