# Unsupervised Medical Image Translation Using Cycle-MedGAN

## TL;DR

Cycle-MedGAN is an unsupervised medical image translation framework that improves image quality by minimizing textural and perceptual discrepancies, enabling better PET-CT translation and MR motion correction without requiring paired datasets.

## Contribution

It introduces non-adversarial cycle losses to enhance unsupervised medical image translation, addressing issues of blurriness and unrealistic details in translated images.

## Key findings

- Outperforms existing unsupervised methods in PET-CT translation
- Effective in MR motion correction
- Produces sharper, more realistic images

## Abstract

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.