Image-to-Image Translation Based on CycleGAN: From CT to MRI

Chenjie Ni

2023

Abstract

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have equal importance in routine examinations. However, in some cases, one certain type may not be available due to limitations in condition. Therefore, it is necessary to establish a connection between CT and MRI images. With the idea of image-to-image translation, this study proposes using the Cycle-Consistent Generative Adversarial Networks (CycleGAN) structure to build a mapping between these two kinds of medical images. Through the combination of Resnet Generator as well as Patch Generative Adversarial Networks (PatchGAN) Discriminator, the CycleGAN model is trained bidirectionally to achieve cyclic translation. Both qualitative and quantitative evaluations are implemented to highlight the model’s effectiveness in transforming CT or MRI images from either direction to the other. In addition, the CycleGAN model excels particularly in cycle consistency, meaning a realistic recovery of the transformed images. Therefore, this study presents a powerful way for achieving mutual conversion between CT and MRI images, which is especially meaningful to diagnosis with limited information. In addition, this research also suggests the potential of image-to-image translation in medical image processing. Future research directions can be set upon this study to further improve the clarity of images and reduce noise so that the generated results can be truly used for clinical diagnosis.

Paper Citation

in Harvard Style

Ni C. (2023). Image-to-Image Translation Based on CycleGAN: From CT to MRI. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 229-233. DOI: 10.5220/0012799400003885

in Bibtex Style

@conference{daml23,
author={Chenjie Ni},
title={Image-to-Image Translation Based on CycleGAN: From CT to MRI},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={229-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012799400003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Image-to-Image Translation Based on CycleGAN: From CT to MRI
SN - 978-989-758-705-4
AU - Ni C.
PY - 2023
SP - 229
EP - 233
DO - 10.5220/0012799400003885
PB - SciTePress