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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
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Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

Young Jin Heo1, Donghyeon Lee1, Junsu Kang1

  • 1Pohang University of Science and Technology (POSTECH), Mechanical engineering, Pohang, 790-784, South Korea.

Scientific Reports
|September 16, 2017
PubMed
Summary
This summary is machine-generated.

A new deep learning pipeline, R-MOD (Real-time Moving Object Detector), enables fast, label-free analysis of cells using imaging flow cytometry (IFC). This technology offers high accuracy for real-time cell identification in microfluidic systems.

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Area of Science:

  • Biotechnology
  • Microscopy
  • Artificial Intelligence

Background:

  • Imaging flow cytometry (IFC) is a high-throughput technology for single-cell image acquisition and population analysis.
  • Microscopic image sensitivity and spatial resolution offer valuable data for single-cell studies in biology.
  • Label-free IFC in microfluidics requires efficient image processing for real-time analysis.

Purpose of the Study:

  • To develop a fast, deep learning-based image-processing pipeline for label-free IFC.
  • To enable real-time identification of single cells in flow within microfluidic devices.
  • To enhance the utility of IFC for biological and clinical applications.

Main Methods:

  • Developed a deep learning pipeline named R-MOD (Real-time Moving Object Detector).
  • Implemented R-MOD for high-throughput, label-free IFC in a microfluidic chip.
  • Utilized a microscope and high-speed camera for image acquisition and real-time processing.

Main Results:

  • R-MOD achieved a processing speed of 500 frames per second (fps).
  • The pipeline demonstrated high accuracy with a mean Average Precision (mAP) of 93.3%.
  • R-MOD successfully identified single-cell images in real-time during flow.

Conclusions:

  • R-MOD is a fast and reliable image-processing tool for label-free IFC.
  • The pipeline simplifies hardware requirements for high-throughput microscopy.
  • R-MOD is poised to become a valuable asset for biomedical and clinical research.