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Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial

Ken Y Foo1, Bryan Shaddy2, Javier Murgoitio-Esandi2

  • 1BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, WA, Australia; Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA, Australia.

Computer Methods and Programs in Biomedicine
|August 20, 2024
PubMed
Summary

A new conditional generative adversarial network (cGAN) method improves cell elasticity imaging accuracy and resolution. This advanced technique enhances the analysis of cell mechanics for better understanding of cell function and disease progression.

Keywords:
Cellular spheroidConfocal fluorescence microscopyElastographyFinite element methodGenerative adversarial networksOptical coherence microscopy

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Area of Science:

  • Biomedical Optics
  • Cellular Mechanics
  • Machine Learning in Imaging

Background:

  • Accurate imaging of cellular mechanical properties is crucial for understanding cell function and disease.
  • Existing mechano-microscopy methods have limitations in accuracy and precision due to simplifying assumptions.
  • There is a need for advanced techniques that can generate high-fidelity elasticity images.

Purpose of the Study:

  • To investigate the feasibility of using a conditional generative adversarial network (cGAN) for elasticity imaging.
  • To develop a cGAN model capable of generating accurate elasticity images from phase difference data.
  • To compare the performance of the cGAN method against the traditional algebraic method.

Main Methods:

  • Generated 30,000 artificial elasticity and corresponding phase difference images using finite element analysis.
  • Trained a cGAN model on simulated data of cell spheroids in hydrogel.
  • Evaluated the cGAN using both simulated and real experimental data from mechano-microscopy of MCF7 breast tumor spheroids.

Main Results:

  • The cGAN achieved a lower root mean square error (median: 3.47 kPa) compared to the algebraic method (median: 4.91 kPa) on simulated data.
  • Elasticity images generated by the cGAN showed higher resolution and improved robustness to noise.
  • The cGAN identified features resembling cell nuclei, correlating with other imaging modalities.

Conclusions:

  • The cGAN method significantly outperforms the algebraic method in elasticity image accuracy and spatial resolution.
  • The developed cGAN demonstrates superior sensitivity and robustness to noise for both simulated and experimental data.
  • This approach offers a promising advancement for high-resolution mechanical property imaging of cells and tissues.