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High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
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Enhancing resolution and contrast in fibre bundle-based fluorescence microscopy using generative adversarial network.

Amir Mohammad Ketabchi1, Berna Morova2, Yiğit Uysalli3,4

  • 1Department of Electrical and Electronics Engineering, Koç University, Istanbul, Türkiye.

Journal of Microscopy
|April 2, 2024
PubMed
Summary
This summary is machine-generated.

Generative adversarial networks (GANs) enhance fluorescence imaging resolution and contrast in fibre bundle (FB) endoscopes. This deep learning approach, trained with multifocal structured illumination microscopy (MSIM) data, significantly improves image quality without extra hardware.

Keywords:
GANbiological imagingdeep learning modelfibre bundle‐based fluorescence microscopyimage‐to‐image translationmultifocal structured illumination microscopy (MSIM)

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Area of Science:

  • Biomedical Optics
  • Microscopy
  • Artificial Intelligence

Background:

  • Fibre bundle (FB) endoscopes are crucial for minimally invasive biological and medical imaging.
  • Limited numerical aperture (NA) and fibre core size restrict resolution and contrast in FB fluorescence microscopy.

Purpose of the Study:

  • To enhance resolution and contrast in fluorescence images acquired via fibre bundle endoscopes.
  • To leverage generative adversarial networks (GANs) for image quality improvement.

Main Methods:

  • Fabrication of in-house, high-NA fibre bundles.
  • Development of a fibre bundle-based multifocal structured illumination microscope (MSIM) using a digital micromirror device (DMD).
  • Training a GAN model using MSIM data for image-to-image translation.

Main Results:

  • The GAN model effectively transformed wide-field images into high-resolution MSIM images.
  • GAN-generated images showed significant improvements in both contrast and resolution compared to original wide-field images.
  • No additional optical hardware was required post-training for GAN image enhancement.

Conclusions:

  • GAN-based models trained with MSIM data show strong potential for enhancing wide-field fluorescence imaging in fibre bundle endoscopes.
  • This approach offers a pathway to overcome inherent limitations of fibre bundle microscopy.
  • The study demonstrates a practical application of deep learning in improving biomedical imaging resolution and contrast.