Article,
Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition
Contributors
Dziegiel, Morten Hanefeld
0000-0001-8034-1523
[2]
[3]
Christensen, Anders Nymark
0000-0002-3668-3128
[1]
Berg-Sørensen, Kirstine
0000-0002-9977-3980
(Corresponding author)
[1]
[4]
Affiliations
- [1] Technical University of Denmark [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
- [2] Copenhagen University Hospital [NORA names: Capital Region of Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
- [3] University of Copenhagen [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
- [4] Kongens Lyngby, Denmark [NORA names: Denmark; Europe, EU; Nordic; OECD]
Abstract
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.
Keywords
SlowFast,
accuracy,
adult blood cells,
adult red blood cells,
behavior,
blood cells,
cell classification,
cells,
chip,
classification,
cytometry,
deformability cytometry,
deformation,
differences,
donor,
fetal blood cells,
fetal red blood cells,
flow,
hydrodynamic deformation,
injection,
microfluidic chip,
molded microfluidic chip,
no significant difference,
recognition,
red blood cells,
significant difference,
temporal behavior,
video,
video recognition
