Machine Vision-based
Cell Sorting

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

A) 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.

In Silico Labeling

B) Machine

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

C) 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.

Core technology: Ghost Cytometry (GC) – Machine Learning-based Sorting

Intro 01

2D image information is collected as cells pass through a structured illumination via proprietary optic designs

Intro 02

… and recorded as a compressed 1D temporal waveform “Ghost Motion Imaging” (GMI)

Intro 03

GMI waveforms are directly analyzed by machine learning-based models for high-speed analysis and sorting, bypassing image reconstruction (first for classifier training then actual analysis of samples)

Intro 04

Target cells (both live and fixed) are gently collected using fluid pressure

Workflow of supervised
machine learning

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



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.



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

3In 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.