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

Lookup NU author(s): Carly Knill, Andrew Fuller, Dr Louise MichaelisORCiD, Professor Andrew FilbyORCiD


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© 2019 International Society for Advancement of CytometryImaging 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.

Publication metadata

Author(s): Lippeveld M, Knill C, Ladlow E, Fuller A, Michaelis LJ, Saeys Y, Filby A, Peralta D

Publication type: Article

Publication status: Published

Journal: Cytometry Part A

Year: 2019

Volume: 97

Issue: 3

Pages: 308-319

Print publication date: 06/03/2020

Online publication date: 05/11/2019

Acceptance date: 02/10/2019

ISSN (print): 1552-4922

ISSN (electronic): 1552-4930

Publisher: John Wiley & Sons, Inc.


DOI: 10.1002/cyto.a.23920


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