Abstract #3190

Deep Learning-based Generative Adversarial Registration NETwork (GARNET) for Hepatocellular Carcinoma Segmentation: Multi-center Study

Hang Yu¹, Rencheng Zheng¹, Weibo Chen², Ruokun Li³, Huazheng Shi⁴, Chengyan Wang⁵, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Philips Healthcare, Shanghai, China, ³Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁴Shanghai Universal cloud imaging dignostic center, Shanghai, China, ⁵Human Phenome Institute, Fudan University, ShangHai, China

Synopsis

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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