This study proposed a deep learning-based generative adversarial registration network (GARNET) for multi-contrast liver image registration and evaluated its value for hepatocellular carcinoma (HCC) segmentation. We used generative adversarial net (GAN) to synthesize images from diffusion-weighted imaging (DWI) to dynamic contrast-enhanced (DCE) and then applied for deformable registration on the synthesized DCE images. A total of 607 cirrhosis patients from 5 centers (401 HCC patients) were included in this study. We compared the proposed method with symmetric image normalization (SyN) registration and VoxelMorph. Experimental results demonstrated that GARNET improved the registration performances significantly and yielded better segmentation of HCC lesions.
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