28 Oct 20. Featured Paper
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis.
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 CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images & data from the source domain, even with additional image alignment losses.
This is because the CycleGAN generator network can encode the relative deformations & noises associated to different domains.
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), & locally learned by modified deformable convolutional layers.
Experiment results demonstrated that our method can achieve better alignment between the source & target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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Featured paper: DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. @EdinUniCVS @SchoolOfEng_UoE #ImageSynthesis