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High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

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Single-shot optically sectioned fluorescence endomicroscopy using unsupervised RCAN-CycleGAN.

Chia-Yu Lin, Yu-Hsin Chia, Sunil Vyas

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces an unsupervised deep learning framework (RCAN-CycleGAN) to create high-contrast optical sectioning images from single-shot wide-field endomicroscopy. This computationally achieved method eliminates the need for complex hardware and multiple exposures, enabling real-time imaging.

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Area of Science:

Biomedical optics
Medical imaging
Computational imaging

Background:

Optical sectioning endomicroscopy enhances contrast but requires complex setups and multiple exposures.
Acquiring paired data for supervised deep learning in vivo is challenging.
Conventional methods increase acquisition time and system complexity.

Purpose of the Study:

To develop an unsupervised deep learning framework for generating high-contrast, optically sectioned images from single-shot wide-field endomicroscopy.
To overcome the limitations of conventional optical sectioning techniques, including hardware complexity and data acquisition challenges.
To enable real-time, single-shot optically sectioned imaging.

Main Methods:

An unsupervised RCAN-CycleGAN framework was developed to translate single-shot wide-field fluorescence endomicroscopy images into HiLo images.
The model was trained on unpaired wide-field and HiLo images from various specimens (fluorescent beads, mouse brain, plant tissues).
The framework does not require structured illumination hardware or paired training data.

Main Results:

The RCAN-CycleGAN achieved high-fidelity optical sectioning computationally, generating HiLo-quality output from single-shot wide-field images.
Performance metrics on paired test data showed an average PSNR of 32.2 dB and SSIM of 0.9, significantly outperforming raw wide-field inputs.
The proposed method demonstrated superior performance compared to unsupervised baselines (CUT, CycleGAN variants) and generalized well to unseen in vivo data.

Conclusions:

High-fidelity optical sectioning is achievable computationally from conventional wide-field endomicroscopy using an unsupervised, unpaired approach.
The RCAN-CycleGAN framework offers a compact, real-time, single-shot solution for optically sectioned imaging.
This method has the potential to facilitate advanced imaging in clinical and resource-limited settings.