DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

Chengjia Wang, Guang Yang, Giorgos Papanastasiou, Sotirios A. Tsaftaris, David E Newby, Calum Gray, Gillian Macnaught, Tom MacGillivray

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGANbased synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT
and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
Original languageEnglish
JournalInformation Fusion
Early online date23 Oct 2020
Publication statusE-pub ahead of print - 23 Oct 2020


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