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Title: Brain cell laser powered by deep-learning-enhanced laser modes
Authors: Qiao, Zhen
Sun, Wen
Zhang, Na
Ang, Randall
Wang, Wenjie
Chew, Sing Yian
Chen, Yu-Cheng
Keywords: Engineering::Bioengineering
Issue Date: 2021
Source: Qiao, Z., Sun, W., Zhang, N., Ang, R., Wang, W., Chew, S. Y. & Chen, Y. (2021). Brain cell laser powered by deep-learning-enhanced laser modes. Advanced Optical Materials, 9(22), 2101421-.
Project: A2084c0063
Journal: Advanced Optical Materials
Abstract: Single cellular lasers have recently attracted tremendous research due to their outstanding lasing characteristics for cell sensing and tracking. Thanks to enhanced light−cell interactions in Fabry–Pérot microcavities, transverse laser modes from cellular lasers are highly correlated to the spatial biophysical properties of cells. However, the huge complexity and randomness of laser modes set a critical challenge towards practical applications in cell analysis. In this study, deep learning is applied to unravel the complex laser modes generated from single-cell lasers by establishing the correlation between laser modes and cellular physical properties. Primary cells extracted from rat brains and cell-like droplets are investigated and trained through a convolutional neuron network based on laser mode images. Detailed simulations and experiments are conducted to study the effect of cell size on laser modes. Predictions of cell diameters with a sub-micron accuracy are achieved with deep learning. Finally, the potential application of using deep-learning-enhanced laser modes for cell classification is demonstrated. Neuron and glial cells extracted from rat brains are classified through hyperspectral images of laser modes. The results demonstrate that deep learning has the potential to enable laser modes with biological significance and functions, offering new possibilities for biophotonic applications.
ISSN: 2195-1071
DOI: 10.1002/adom.202101421
Schools: School of Electrical and Electronic Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
School of Chemical and Biomedical Engineering 
Rights: © 2021 Wiley-VCH GmbH. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
LKCMedicine Journal Articles
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