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)

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: Hou, Dr Lianping and Marsh, Professor John
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
302617		Optically controlled THz phased array antennas	John Marsh	Engineering and Physical Sciences Research Council (EPSRC)	EP/R042578/1	ENG - Electronics & Nanoscale Engineering