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U-Patch GAN: A Medical Image Fusion Method Based on GAN
Chao Fan1,2, Hao Lin3, Yingying Qiu4
1School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou City, 450001, Henan Province, China.
Journal of Digital Imaging
|August 29, 2022
Summary
This study introduces U-Patch GAN, a novel generative adversarial network for self-supervised multimodal brain image fusion. The model enhances diagnostic accuracy by improving the quality and information retention of fused medical images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Multimodal medical imaging aids diagnosis but fusion algorithms often lose source image information.
- Accurate fusion of multimodal brain images is crucial for complex diagnoses.
Purpose of the Study:
- To develop an end-to-end generative adversarial network (GAN) model for high-quality, self-supervised multimodal brain image fusion.
- To enhance the retention of functional and structural information in fused medical images.
Main Methods:
- Developed U-Patch GAN, utilizing U-net as generator and PatchGAN discriminator for high-frequency information focus.
- Applied spectral norm for Lipschitz continuity and introduced novel adversarial and feature losses (F-norm based).
- Evaluated on public datasets, comparing single-slice and continuous-slice image fusion against six mainstream methods.
Main Results:
- The U-Patch GAN model significantly enhanced fused image quality compared to existing methods.
- Clinical utility assessments confirmed the practical value of the generated fused images.
- Verified the effectiveness of proposed adversarial and feature loss functions.
Conclusions:
- The U-Patch GAN model offers superior multimodal brain image fusion, improving diagnostic capabilities.
- The proposed loss functions and network architecture effectively preserve source image details.
- This approach advances self-supervised learning in medical image analysis.
