Gold-viral particle identification by deep learning in wide-field photon scattering parametric images
Article
Zhao, H., Ni, B., Jin, X., Zhang, H., Hou, J., Hou, L., Marsh, J., Dong, L., Li, S., Gao, X., Shi, D., Liu, X. and Xiong, J. 2022. Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. Applied Optics. 61 (2), pp. 546-553. https://doi.org/10.1364/AO.445953
Type | Article |
---|---|
Title | Gold-viral particle identification by deep learning in wide-field photon scattering parametric images |
Authors | Zhao, H., Ni, B., Jin, X., Zhang, H., Hou, J., Hou, L., Marsh, J., Dong, L., Li, S., Gao, X., Shi, D., Liu, X. and Xiong, J. |
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. |
Publisher | Optica Publishing Group |
Journal | Applied Optics |
ISSN | 1559-128X |
Electronic | 2155-3165 |
Publication dates | |
Online | 07 Jan 2022 |
10 Jan 2022 | |
Publication process dates | |
Deposited | 26 Jan 2022 |
Submitted | 13 Oct 2021 |
Accepted | 06 Dec 2021 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | © 2022 Optica Publishing Group. |
Digital Object Identifier (DOI) | https://doi.org/10.1364/AO.445953 |
Language | English |
https://repository.mdx.ac.uk/item/89q37
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