SC-GAN: Structure Consistent GAN for Modality Transfer with FFT and Multi-Scale Perception

Ruiling Xi1; Yinglin Zhang2; Ruibin Bai2; Risa Higashita1; Jiang Liu1

10.1109/ISBI53787.2023.10230436

2023

1945-7928

978-1-6654-7359-0

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

2023-April

1-5

18-21 April 2023

Cartagena, Colombia

The quality of the cornea endothelial microscopy image is critical for clinical analysis. Although the noncontact specular microscope is more user-friendly than the contact confocal microscope, the imaging quality of the specular microscope is lower. The modality transfer is a promising solution for image quality enhancement. This paper proposes a Structure Consistent Generative Adversarial Network (SC-GAN) to transfer the imaging style from the specular microscope to the confocal microscope. Specifically, we use the Fourier frequency domain consistency to preserve cell structure and propose a multi-scale perception discriminator to improve model robustness under cell size variation. Experiment results prove the effectiveness of our method.

Corneal Endothelial Cell Modality transfer Structure Consistency Generative Adversarial Network

第一

CPCI-S ; EI

WOS:001062050500114

20233914806234

Endothelial cells ; Frequency domain analysis ; Image enhancement ; Image quality ; Microscopes

Biology:461.9 ; Artificial Intelligence:723.4 ; Optical Devices and Systems:741.3 ; Mathematical Transformations:921.3

IEEE

1.Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Computer Science, University of Nottingham Ningbo China, Ningbo, China

Ruiling Xi,Yinglin Zhang,Ruibin Bai,et al. SC-GAN: Structure Consistent GAN for Modality Transfer with FFT and Multi-Scale Perception[C],2023:1-5.