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WSCNet: Biomedical Image Recognition for Cell Encapsulated Microfluidic Droplets.

Xiao Zhou1, Yuanhang Mao1, Miao Gu1

  • 1Department of Automation, Tsinghua University, Beijing 100084, China.

Biosensors
|August 25, 2023
PubMed
Summary

This study presents a microfluidic system using a weakly supervised cell counting network (WSCNet) to accurately assess single-cell encapsulation in droplets. The WSCNet system effectively distinguishes droplet quality and cell locations for improved single-cell analysis.

Keywords:
convolutional neural network (CNN)droplet microfluidicsimage recognitionsingle-cell encapsulation

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Area of Science:

  • Biotechnology
  • Microfluidics
  • Single-cell analysis

Background:

  • Microfluidic droplets are crucial for single-cell analysis, acting as independent microreactors for phenotype and genotype studies.
  • Accurately controlling and monitoring the number of cells per droplet is challenging, particularly minimizing multi-cell encapsulation.

Purpose of the Study:

  • To develop and validate a microfluidic system integrated with a weakly supervised cell counting network (WSCNet) for evaluating microfluidic droplet quality.
  • To accurately recognize the locations of encapsulated cells within droplets without requiring supervised location data.

Main Methods:

  • Demonstration of a microfluidic system incorporating a WSCNet for droplet generation and quality assessment.
  • Systematic verification of the WSCNet approach using microfluidic droplets from three distinct microfluidic structures.

Main Results:

  • The WSCNet approach achieved high accuracy in distinguishing droplet encapsulations (F1 score > 0.88) and locating individual cells (accuracy > 89%).
  • The system accurately predicted the probability of single-cell encapsulation, showing strong agreement with passive methods (RSS < 0.5).

Conclusions:

  • The developed microfluidic system provides a robust platform for the quantitative assessment of encapsulated microfluidic droplets.
  • This WSCNet-based approach enhances the reliability of single-cell analysis by improving the control and evaluation of droplet encapsulation.