A Clinical Tool for Automated Flow Cytometry Based on Machine Learning Methods

  • Claude TakengaEmail author
  • Michael Dworzak
  • Markus Diem
  • Rolf-Dietrich Berndt
  • Erling Si
  • Michael Brandstoetter
  • Leonid Karawajew
  • Melanie Gau
  • Martin Kampel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10209)


Clinical researchers working in flow cytometry (FCM) nowadays experience increasing demands to perform experiments that involve high throughput, rare event analysis and detailed immunophenotyping. Beckman Coulter and Becton Dickinson offer multi-use flow cytometry sorters that can analyze up to 70K EPS (events per seconds) with more than nine parameters enabled. While this multi-parametric feature provides a great power for hypothesis testing, it also generates a vast amount of data, which is analyzed manually through a processing called gating. For large experiments, this manual gating turns out to be time consuming and requires intensive operator training and experience. The lack of required expertise leads to wrong interpretation of data, thus a wrong therapy course for the case of patients with acute lymphoblastic leukemia (ALL) is followed. This paper aims to present a pipeline-software, as a ready-to-use machine learning based automated FCM assessment tool for the daily clinical practice for patients with ALL. The new system increases accuracy in assessment of FCM based on minimal residual disease (MRD) method in samples analyzed by conventional operator-based gating since computer-aided analysis potentially has a higher power due to the use of the whole multi-parametric FCM-data space at once instead of using methods restricted to two-dimensional decision rules. The tool is implemented as a telemedical network for analysis, clinical follow-up, treatment monitoring of leukemia and allows dissemination of automated FCM-MRD analysis to medical centres in the world.


Clinical tool Flow cytometry Leukemia Machine learning Telematics platform Telemedicine 



The AutoFLOW project is funded by Marie Curie Industry Academia Partnerships & Pathways (FP7-MarieCurie-PEOPLE-2013- IAPP) under the grant no.610872.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Claude Takenga
    • 1
    Email author
  • Michael Dworzak
    • 2
    • 3
  • Markus Diem
    • 1
    • 2
  • Rolf-Dietrich Berndt
    • 4
  • Erling Si
    • 4
  • Michael Brandstoetter
    • 5
  • Leonid Karawajew
    • 6
  • Melanie Gau
    • 1
  • Martin Kampel
    • 1
  1. 1.Computer Vision LabTU WienViennaAustria
  2. 2.Labdia Labordiagnostik GmbHViennaAustria
  3. 3.Children’s Cancer Research InstituteMedical University of ViennaViennaAustria
  4. 4.Infokom GmbHNeubrandenburgGermany
  5. 5.CogVis Software and Consulting GmbHViennaAustria
  6. 6.Charité – Universitaetmedizin BerlinBerlinGermany

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