High throughput hemogram of T cells using digital holographic microscopy and deep learning : Optics Continuum
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.
Citation
Gupta , R K , Hempler , N , Malcolm , G P A , Dholakia , K & Powis , S J 2023 , ' High throughput hemogram of T cells using digital holographic microscopy and deep learning : Optics Continuum ' , Optics Continuum , vol. 2 , no. 3 , pp. 670-682 . https://doi.org/10.1364/OPTCON.479857
Publication
Optics Continuum
Status
Peer reviewed
ISSN
2770-0208Type
Journal article
Description
Funding: Engineering and Physical Sciences Research Council (EP/P030017/1, EP/R004854/1); Medical Research Scotland (PhD-873-2015); Australian Research Council (FL210100099).Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.