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Related Experiment Video

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Ultrawidefield-to-Conventional Fundus Image Translation with Scaled Feature Registration and Distorted Vessel

JuChan Kim¹, Junghyun Bum², Duc-Tai Le²

¹Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Bioengineering (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

This study introduces a new method to translate ultrawidefield fundus images (UFIs) into conventional fundus images (CFIs), improving diagnostic support in ophthalmology. The enhanced technique reduces patient burden by potentially replacing dual-imaging approaches.

Keywords:

fundus image image registration image translation multi-modal imaging paired-learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Conventional fundus (CF) and ultrawidefield fundus (UF) imaging are crucial in ophthalmology.
Existing fundus image translation methods lack clinical viability, accuracy, and detail.
Paired data collection is limited, and unpaired models cause distortions like hallucinations.

Purpose of the Study:

To enhance diagnostic support in ophthalmology using deep learning.
To develop a method for translating UF images (UFIs) into CF images (CFIs).
To potentially replace dual-imaging approaches, reducing patient financial and temporal burdens.

Main Methods:

Leveraged same-day UFI-CFI image pairs from the same patient to minimize distortion.
Employed scaled feature registration and distorted vessel correction for effective alignment.
Developed an enhanced modality transformation method for UFI to CFI conversion.

Main Results:

Generated CFIs demonstrated enhanced image quality and better retinal area representation.
Preserved crucial disease-related details from UFIs for accurate diagnosis.
Achieved 18.2% reduction in MSE, 7.2% increase in PSNR, and 12.7% improvement in SSIM compared to existing methods.

Conclusions:

The proposed method significantly improves fundus image translation accuracy and detail preservation.
Generated CFIs are nearly indistinguishable from real CFIs, validated by ophthalmologists.
This technique offers a promising alternative to dual-imaging, enhancing clinical efficiency and patient care.