Generative Adversarial Registration Network for Multi-Contrast Liver MRI Registration and Added Value to Hepatocellular Carcinoma Segmentation: A Multicentre Study

Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging are essential for the diagnosis of hepatocellular carcinoma (HCC) but suffer from severe displacement due to respiratory and cardiac motions. The currently available registration algorithms do not align the multi-contrast liver images satisfactorily, which hinders the efficacy of automatic lesion segmentation and radiomics analysis. We proposed a deep learning-based generative adversarial registration network (GARNET) for multi-contrast liver image registration and explored the added value on HCC lesion segmentation in patients with cirrhosis. The proposed model applied a pre-trained generative adversarial network to synthesize DCE images from DWI and then used diffeomorphic based symmetric image normalization (SyN) algorithm for registration. Furthermore, an attention-based U-net (AU-NET) was used to segment the HCC lesion from registered DWI and DCE images. GARNET was developed and validated on 901 patients collected between January 2013 and February 2021 across five medical centers, including 517 HCC patients, total of 597 HCCs, and every case was interpolated to 32 slices. GARNET achieved the highest liver registration Dice (0.812 +/- 0.045) among all comparative methods, and the lowest tumor target registration error (3.470 +/- 1.652 mm) for DWI/DCE registrations. In addition, the performance of HCC segmentation was significantly improved when using registered images from GARNET, with Dice improvements from 0.36 +/- 0.22 to 0.45 +/- 0.22; 0.53 +/- 0.25 to 0.58 +/- 0.21; and 0.44 +/- 0.26 to 0.56 +/- 0.19 in three validation centers. Experimental results showed that GARNET significantly improved the liver registration performances among multi-contrast images, thereby improving the further identification and segmentation of HCCs.