Gold-viral particle identification by deep learning in wide-field photon scattering parametric images

Zhao, H. et al. (2022) Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. Applied Optics, 61(2), pp. 543-553. (doi: 10.1364/AO.445953)

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Abstract

The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution instruments such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are required. In this paper, we propose a new method for identifying virus particles based on polarization parametric indirect microscopic imaging (PIMI) and deep learning techniques. By introducing an abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), the strength of the photon scattering signal can be magnified. After acquiring the PIMI images, a deep learning method was applied to identify discriminating features and classify the virus particles, using electron microscopy (EM) images as the ground truth. Experimental results confirm that gold-virus particles can be identified in PIMI images with a high level of confidence.

Item Type:Articles
Additional Information:Funding. National Key Scientific Instrument and Equipment Development Projects of China (61827814); Beijing Municipal Natural Science Foundation (Z190018); National Natural Science Foundation of China (62105155); Natural Science Foundation of Jiangsu Province (BK20210326); Ministry of Education collaborative project (B17023); Engineering and Physical Sciences Research Council (EP/R042578/1); Royal Society (IEC/NSFC/181557).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Marsh, Professor John and Hou, Dr Lianping
Authors: Zhao, H., Ni, B., Jin, X., Zhang, H., Hou, J. J., Hou, L., Marsh, J. H., Dong, L., Li, S., Gao, X., Shi, D., Xiu, X., and Xiong, J.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Applied Optics
Publisher:Optical Society of America
ISSN:1559-128X
ISSN (Online):2155-3165
Copyright Holders:Copyright © 2022 Optica Publishing Group
First Published:First published in Applied Optics 61(2):543-553
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
302617Optically controlled THz phased array antennasJohn MarshEngineering and Physical Sciences Research Council (EPSRC)EP/R042578/1ENG - Electronics & Nanoscale Engineering