Single-cell imaging dataset of healthy human white blood cells

ABSTRACT: Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5000 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 realise this automated, stain-free approach, advanced machine learning methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical machine learning approach based on manually engineered features, and a deep learning 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 8 white blood cell types. Four machine learning classifiers were evaluated on stain-free imagery with stratified 5-fold cross-validation. On the white blood cell dataset the best obtained results were 0.778 and 0.703 balanced accuracy for classical machine learning and deep learning, respectively. We conclude that classifying cell types based on only stain-free images is possible with all four classifiers. Noteworthy, we also find that the deep learning approaches tested in this work do not outperform the approaches based on manually engineered features.

ORGANISM(S): Homo sapiens (human)

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PROVIDER: S-BIAD452 | bioimages |

REPOSITORIES: bioimages