Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 1.44 MB, PDF document

  • Peter Johannes Tejlgaard Kampen
  • Gustav Ragnar Støttrup-Als
  • Nicklas Bruun-Andersen
  • Joachim Secher
  • Freja Høier
  • Anne Todsen Hansen
  • Dziegiel, Morten Hanefeld
  • Anders Nymark Christensen
  • Kirstine Berg-Sørensen

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.

Original languageEnglish
Article number5
JournalBiomedical Microdevices
Issue number1
Number of pages9
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

    Research areas

  • Deformation, Microfluidic flow cytometry, Neural network, Red blood cell, SlowFast

ID: 377992186