TECHNOLOGY

Integrating advanced optics,
machine learning and biotechnology to improve lives

Machine Vision-based Cell Sorting (ViCS)

Characterizing and sorting cells based on image information at
record high-throughput rates by integrating a novel ultrafast
imaging technique with artificial intelligence.

How It Works

How It Works
  • i) Image acquisition
    Image acquisition

    Image information of each cell is recorded as a compressed temporal waveform with a single pixel detector as the cell passes through an illumination pattern projected onto a microchannel.

  • ii) Machine learning-based classification
    Machine learning-based classification

    A trained AI model predicts the cell class based on the waveform.

  • iii) Sorting
    Sorting

    The classified cells are gently isolated using fluid pressure.

* Ota et al, Science, 2018 Jun 15;360(6394):1246-1251. doi: 10.1126/science.aan0096.

Workflow of supervised machine learning

The workflow outlines key steps involved in developing a machine learning model in ViCS technology.

  • Labeling
    Labeling

    Image information of each cell is acquired as a compressed temporal waveform using the novel imaging technique. The acquired waveforms are then labeled using biomarkers or other molecular labels.

  • Modeling
    Modeling

    A machine-learning model is developed based on the labeled waveforms.

  • In Silico Labeling
    In Silico Labeling

    The developed machine-learning model predicts labels by evaluating the waveforms of cells. This machine-learning approach enables image inference at an unprecedented speed.

  • Labeling
    Labeling

    Image information of each cell is acquired as a compressed temporal waveform using the novel imaging technique. The acquired waveforms are then labeled using biomarkers or other molecular labels.

  • Modeling
    Modeling

    A machine-learning model is developed based on the labeled waveforms.

  • In Silico Labeling
    In Silico Labeling

    The developed machine-learning model predicts labels by evaluating the waveforms of cells. This machine-learning approach enables image inference at an unprecedented speed.

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