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Virtual optofluidic time-stretch quantitative phase imaging
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DOI：10.1063/1.5134125.png)
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Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a potent tool for biomedical applications as it enables high-throughput QPI of numerous cells for large-scale single-cell analysis in a label-free manner. However, there are a few critical limitations that hinder OTS-QPI from being widely applied to diverse applications, such as its costly instrumentation and inherent phase-unwrapping errors. Here, to overcome the limitations, we present a QPI-free OTS-QPI method that generates virtual phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images. Specifically, our trained generative adversarial network model generated virtual phase images with high similarity (structural similarity index >0.7) to their corresponding real phase images. This was also supported by our successful classification of various types of leukemia cells and white blood cells via their virtual phase images. The virtual OTS-QPI method is highly reliable and cost-effective and is therefore expected to enhance the applicability of OTS microscopy in diverse research areas, such as cancer biology, precision medicine, and green energy.
Keyword:
HIGH-THROUGHPUT
LABEL-FREE
SINGLE-CELL
MICROSCOPY
期刊
IF:
5.3
论文数:
1.5K
被引数:
5.5K
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引用论文
Reference-free polarization-sensitive quantitative phase imaging using single-point optical phase conjugation.png)
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