Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition
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Flow based deformation cytometry has shown potential for cell classification. We demonstrate the principle with an injection moulded microfluidic chip from which we capture videos of adult and fetal red blood cells, as they are being deformed in a microfluidic chip. Using a deep neural network - SlowFast - that takes the temporal behavior into account, we are able to discriminate between the cells with high accuracy. The accuracy was larger for adult blood cells than for fetal blood cells. However, no significant difference was observed between donors of the two types.
Originalsprog | Engelsk |
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Artikelnummer | 5 |
Tidsskrift | Biomedical Microdevices |
Vol/bind | 26 |
Udgave nummer | 1 |
Antal sider | 9 |
ISSN | 1387-2176 |
DOI | |
Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
Open access funding provided by Technical University of Denmark Microfluidic chips were obtained through the Strategic Research Centre PolyNano, Danish Council for Strategic Research (Grant no. 10-092322/DSF); High-speed camera acquired through grant from The Carlsberg Foundation (Grant no. 2009-1-0286). KBS is currently supported by Independent Research Fund Denmark (grant no 0135-00142B) and the Novo Nordisk Foundation (grant no NNF20OC0061673).
Publisher Copyright:
© 2023, The Author(s).
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