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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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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Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification.

Jing Sun1,2, Lan Wang2, Qiao Liu3

  • 1School of Microelectronics, Shandong University, Jinan, China.

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|December 7, 2020
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A novel deep learning method uses light scattering to classify Acute Lymphocytic Leukemia (ALL) subtypes without stains. This label-free flow cytometry offers a faster, more accurate approach for ALL subtyping.

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Hematology

Background:

  • Accurate subtyping of Acute Lymphocytic Leukemia (ALL) is crucial for treatment and prognosis.
  • Traditional ALL subtyping methods are labor-intensive and time-consuming.
  • Current flow cytometry methods for ALL subtyping can be costly.

Purpose of the Study:

  • To develop a deep learning-based, label-free method for classifying Acute Lymphocytic Leukemia (ALL) subtypes.
  • To overcome the limitations of conventional ALL diagnostic techniques.

Main Methods:

  • Development of a deep learning framework (Inception V3-SIFT-Scattering Net or ISSC-Net).
  • Utilizing label-free light scattering imaging flow cytometry with hydrodynamic focusing.
  • Acquiring two-dimensional (2D) light scattering patterns from single ALL cells.

Main Results:

  • The ISSC-Net achieved high-precision classification of T-ALL and B-ALL cell lines.
  • Classification accuracy reached 0.993 ± 0.003.
  • Demonstrated the potential for automatic and accurate subtyping of unstained ALL cells.

Conclusions:

  • Deep learning-based light scattering flow cytometry provides a promising approach for ALL subtyping.
  • This label-free method offers an efficient and accurate alternative to existing techniques.
  • The developed technology supports automatic and precise classification of unstained ALL samples.