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Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian

Mengmeng Wang1, Lee-Ling Sharon Ong2, Justin Dauwels3

  • 1Nanyang Technological University, Energy Research Institute, Singapore.

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|June 15, 2018
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Summary
This summary is machine-generated.

This study introduces an advanced image analysis system for tracking cell migration in 3D microfluidic devices (MFDs). The system achieves 86.4% association accuracy, offering a valuable tool for biological research.

Keywords:
backward Kalman filterscoarse time-lapse phase-contrast imagesconvolutional neural networksend-point confocal imagesendothelial cell networks trackingmultiple hypothesis tracking

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Area of Science:

  • Cell biology
  • Biomedical engineering
  • Image analysis

Background:

  • Cell migration is crucial for biological processes.
  • Studying cell migration in 3D in vitro environments requires advanced tools.
  • Microfluidic devices (MFDs) offer promising platforms for cell culture and research.

Purpose of the Study:

  • To develop an automated image analysis system for extracting cell behaviors from 3D MFDs.
  • To improve the accuracy of cell tracking in complex 3D microenvironments.
  • To provide a quantitative tool for analyzing cell migration in angiogenic vessels.

Main Methods:

  • Development of an image analysis system using phase-contrast microscopy.
  • Application of convolutional neural networks for cell detection.
  • Integration of backward Kalman filtering and multiple hypothesis tracking for cell candidate association.
  • Incorporation of prior knowledge on vessel formation and cell proliferation rates.

Main Results:

  • The proposed system achieves an 86.4% cell association accuracy.
  • The developed algorithm demonstrates higher accuracy compared to existing cell tracking methods.
  • Successful application in cell culture experiments within 3D MFDs.

Conclusions:

  • The developed image analysis system is effective for studying cell migration in 3D MFDs.
  • The system provides a promising quantitative tool for microscopy in MFD research.
  • This approach enhances the study of cell behaviors in complex biological models.