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Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.

Authors
  • Lippeveld, Maxim1, 2
  • Knill, Carly3
  • Ladlow, Emma3, 4
  • Fuller, Andrew3
  • Michaelis, Louise J5, 6
  • Saeys, Yvan1, 2
  • Filby, Andrew3
  • Peralta, Daniel1, 2
  • 1 Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium. , (Belgium)
  • 2 Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium. , (Belgium)
  • 3 Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
  • 4 Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • 5 Great North Children's Hospital, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • 6 Institute of Health and Society, University of Newcastle, Newcastle upon Tyne, UK.
Type
Published Article
Journal
Cytometry Part A
Publisher
Wiley (John Wiley & Sons)
Publication Date
Nov 05, 2019
Identifiers
DOI: 10.1002/cyto.a.23920
PMID: 31688997
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain-free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain-free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain-free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain-free imagery with stratified 5-fold cross-validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain-free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry. © 2019 International Society for Advancement of Cytometry.

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