High throughput hemogram of T cells using digital holographic microscopy and deep learning: Optics Continuum

Roopam K. Gupta, Nils Hempler, Graeme P. A. Malcolm, Kishan Dholakia*, Simon J. Powis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

T cells of the adaptive immune system provide effective protection to the human body against numerous pathogenic challenges. Current labelling methods of detecting these cells, such as flow cytometry or magnetic bead labelling, are time consuming and expensive. To overcome these limitations, the label-free method of digital holographic microscopy (DHM) combined with deep learning has recently been introduced which is both time and cost effective. In this study, we demonstrate the application of digital holographic microscopy with deep learning to classify the key CD4+ and CD8+ T cell subsets. We show that combining DHM of varying fields of view, with deep learning, can potentially achieve a classification throughput rate of 78,000 cells per second with an accuracy of 76.2% for these morphologically similar cells. This throughput rate is 100 times faster than the previous studies and proves to be an effective replacement for labelling methods.
Original languageEnglish
Pages (from-to)670-682
Number of pages13
JournalOptics Continuum
Volume2
Issue number3
DOIs
Publication statusPublished - 9 Mar 2023

Keywords

  • Analytical techniques
  • Diode pumped lasers
  • Holographic microscopy
  • Holographic techniques
  • Imaging techniques
  • Scanning electron microscopy

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