Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks

• URL: http://arxiv.org/abs/2308.14066v2
• Date: Tue, 29 Aug 2023 04:59:41 GMT
• Title: Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
• Authors: Xin Yang, Yi Lin, Zhiwei Wang, Xin Li, Kwang-Ting Cheng
• Abstract summary: We propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modalities in a sequential order. Visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods.
• Score: 35.358653509217994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modalities in a sequential order. A method for measuring the synthesis complexity is proposed to automatically determine the synthesis order in our sequential GAN. Images of the modality with a lower complexity are synthesized first, and the counterparts with a higher complexity are generated later. Our sequential GAN is trained end-to-end in a semi-supervised manner. In supervised training, the joint distribution of bi-modality images are learned from real paired images of the two modalities by explicitly minimizing the reconstruction losses between the real and synthetic images. To avoid overfitting limited training images, in unsupervised training, the marginal distribution of each modality is learned based on unpaired images by minimizing the Wasserstein distance between the distributions of real and fake images. We comprehensively evaluate the proposed model using two synthesis tasks based on three types of evaluate metrics and user studies. Visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and reasonable visual quality and clinical significance. Code is made publicly available at https://github.com/hustlinyi/Multimodal-Medical-Image-Synthesis.

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