Unpaired medical image enhancement based on generative adversarial networks

Kezhi Deng; Bo Tao; Jiaxin Hu

doi:10.1117/12.3048287

5 December 2024 Unpaired medical image enhancement based on generative adversarial networks

Kezhi Deng, Bo Tao, Jiaxin Hu

Proceedings Volume 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024); 134181E (2024) https://doi.org/10.1117/12.3048287
Event: 15th International Conference on Information Optics and Photonics (CIOP2024), 2024, Xi’an, China

Abstract

Medical imaging technology has significantly aided clinical decision-making, providing essential diagnostic and treatment information for physicians. Current medical image enhancement methods, based on pix2pix/CycleGAN, can improve image quality but often struggle to maintain uniform illumination and preserve texture details, introducing boundary pseudo-noise. Medical image enhancement faces unique challenges due to most medical image datasets being unpaired. Higher standards are required for illumination and texture in lesion areas. To address these issues, we propose UMIEGAN (Unpaired Medical Image Enhancement Generative Adversarial Networks) for medical image enhancement. In the generator, we introduce the residual shrinkage building unit with the channel-shared thresholds module (DRSN-CS) to suppress artifacts and image noise and extract medical image information through three paths using different convolution kernels. In the discriminator, a dual-scale discriminator is employed to evaluate generated images, enhancing the ability to judge medical images authenticity. We also introduce content-aware loss to improve lesion area details and illumination loss to optimize overall illumination distribution, resulting in smoother medical images. Extensive experiments were conducted on two datasets, the fundus retina dataset and the endoscopic image dataset, to verify the feasibility and accuracy of the proposed method. Results demonstrate that the proposed UMIEGAN outperforms traditional methods and other advanced deep learning methods and shows superior performance in downstream segmentation tasks on the fundus retina dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kezhi Deng, Bo Tao, and Jiaxin Hu "Unpaired medical image enhancement based on generative adversarial networks", Proc. SPIE 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024), 134181E (5 December 2024); https://doi.org/10.1117/12.3048287

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KEYWORDS

Image enhancement

Medical imaging

Image quality

Image segmentation

Light sources and illumination

Convolution

Endoscopy

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