An Attentive-based Generative Model for Medical Image Synthesis
Jiayuan Wang Q. M. Jonathan Wu Farhad Pourpanah
Magnetic resonance (MR) and computer tomography (CT) imaging are valuable
tools for diagnosing diseases and planning treatment. However, limitations such
as radiation exposure and cost can restrict access to certain imaging
modalities. To address this issue, medical image synthesis can generate one
modality from another, but many existing models struggle with high-quality
image synthesis when multiple slices are present in the dataset. This study
proposes an attention-based dual contrast generative model, called
ADC-cycleGAN, which can synthesize medical images from unpaired data with
multiple slices. The model integrates a dual contrast loss term with the
CycleGAN loss to ensure that the synthesized images are distinguishable from
the source domain. Additionally, an attention mechanism is incorporated into
the generators to extract informative features from both channel and spatial
domains. To improve performance when dealing with multiple slices, the
$K$-means algorithm is used to cluster the dataset into $K$ groups, and each
group is used to train a separate ADC-cycleGAN. Experimental results
demonstrate that the proposed ADC-cycleGAN model produces comparable samples to
other state-of-the-art generative models, achieving the highest PSNR and SSIM
values of 19.04385 and 0.68551, respectively. We publish the code at
https://github.com/JiayuanWang-JW/ADC-cycleGAN.