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Time Stretch Quantitative Phase Imaging

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Artificial Intelligence in Label-free Microscopy

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Abstract

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification.

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Authors and Affiliations

  1. University of California Los Angeles, Los Angeles, CA, USA

    Ata Mahjoubfar, Claire Lifan Chen & Bahram Jalali

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  1. Ata Mahjoubfar
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  2. Claire Lifan Chen
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  3. Bahram Jalali
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Mahjoubfar, A., Chen, C.L., Jalali, B. (2017). Time Stretch Quantitative Phase Imaging. In: Artificial Intelligence in Label-free Microscopy. Springer, Cham. https://doi.org/10.1007/978-3-319-51448-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-51448-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51447-5

  • Online ISBN: 978-3-319-51448-2

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