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A weighted feature transfer gan for medical image synthesis

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Abstract

Recent studies have shown that CycleGAN is a highly influential medical image synthesis model. However, the lack of sufficient constraints and the bottleneck layer in auto-encoder network usually lead to blurry image and meaningless features, which may affect medical judgment. In order to synthesize accurate and meaningful medical images, weighted feature transfer GAN (WFT-GAN) is proposed to improve the quality of generated medical image, which is applied to the synthesis of unpaired multi-modal data. WFT-GAN adopts weighted feature transfer (WFT) instead of traditional skip connection to reduce the interference of encoding information on image decoding, while retaining the advantage of skip connection to the information transmission of the generated image. Moreover, the local perceptual adversarial loss combines the VGG feature map and adversarial model to make the local features of the image more meaningful. Experiments in three data sets show that the method in this paper can synthesize higher-quality medical images.

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Acknowledgements

The work is partially supported by the Natural Science Foundation of China (Nos. 61503188), CERNET Innovation Project (NGII20180604) and the Natural Science Foundation of Jiangsu Province (Nos. BK20180727).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu Province, China

    Shuaizhen Yao, Yi Chen & Yanhui Gu

  2. School of Public Administration, Nanjing Agricultural University, Nanjing, Jiangsu Province, China

    Jianhua Tan

  3. Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu Province, China

    Jianhua Tan

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  1. Shuaizhen Yao
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  3. Yi Chen
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  4. Yanhui Gu
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Correspondence to Yi Chen or Yanhui Gu.

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Yao, S., Tan, J., Chen, Y. et al. A weighted feature transfer gan for medical image synthesis. Machine Vision and Applications 32, 22 (2021). https://doi.org/10.1007/s00138-020-01152-8

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