Deep-learning Augmented Reflectance Microscopy for Label-free Multiplexed Cytometry

Shiyi Cheng; Sipei Fu; Yumi Mun Kim; Weiye Song; Yunzhe Li; Yujia Xue; Ji Yi; Ji Yi; Ji Yi; Ji Yi; Lei Tian

doi:10.1364/CLEO_AT.2021.ATu4L.3

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper ATu4L.3
•https://doi.org/10.1364/CLEO_AT.2021.ATu4L.3

Deep-learning Augmented Reflectance Microscopy for Label-free Multiplexed Cytometry

Shiyi Cheng, Sipei Fu, Yumi Mun Kim, Weiye Song, Yunzhe Li, Yujia Xue, Ji Yi, and Lei Tian

CLEO: Applications and Technology 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Machine Learning Enhanced Biological Imaging (ATu4L)

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S. Cheng, S. Fu, Y. M. Kim, W. Song, Y. Li, Y. Xue, J. Yi, and L. Tian, "Deep-learning Augmented Reflectance Microscopy for Label-free Multiplexed Cytometry," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper ATu4L.3.

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Abstract

We develop a label-free multiplexed cytometry technique based on deep-learning-augmented virtual labeling. We demonstrate accurate single-cell structural phenotypes of cell cycles and multi-parametric single-cell profiling across a large cell population from the multiplexed readouts.

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