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Related Experiment Video

Updated: Jul 20, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep

Matan Dudaie1, Itay Barnea1, Noga Nissim1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.

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|July 31, 2023
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Summary

We developed a fast, label-free method for classifying cells using raw digital holograms. This new approach speeds up imaging flow cytometry by analyzing holographic images directly, bypassing lengthy reconstruction steps.

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Area of Science:

  • Biophotonics
  • Computational Biology
  • Cellular Imaging

Background:

  • Imaging flow cytometry is crucial for cell analysis.
  • Current methods often require time-consuming cell profile reconstruction for classification.
  • Label-free techniques are desirable for preserving cell viability and simplifying workflows.

Purpose of the Study:

  • To develop a rapid, label-free cell classification method using raw digital holograms.
  • To bypass the computationally intensive phase profile reconstruction step in holographic imaging flow cytometry.
  • To enhance the speed and robustness of cell classification in high-throughput screening.

Main Methods:

  • Acquisition of raw off-axis digital holograms of cells in flow.
  • Development of a convolutional neural network (CNN) trained on raw holographic images.
  • CNN designed to be invariant to spatial frequencies and fringe directions in holograms.
  • Validation using four distinct cancer cell types.

Main Results:

  • Demonstrated successful label-free classification of individual cells directly from raw holograms.
  • Achieved classification without the need for quantitative phase profile reconstruction.
  • Showcased the CNN's ability to handle variations in holographic fringe patterns.
  • Validated the approach on multiple cancer cell lines.

Conclusions:

  • The presented method significantly accelerates label-free cell classification in imaging flow cytometry.
  • Direct analysis of raw digital holograms offers a faster and potentially more robust alternative to traditional reconstruction-based methods.
  • This approach holds promise for real-time, high-throughput cellular analysis in various biological and clinical applications.