Article,
Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition
Affiliations
- [1] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
- [2] Univ Copenhagen, Dept Clin Med, Copenhagen, Denmark [NORA names: KU University of Copenhagen; University; 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
Deformation,
Microfluidic flow cytometry,
Neural network,
Red blood cell,
SlowFast