  Simultaneous segmentation and detection of liver tumors (hemangioma and
hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic
resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it
is still a challenging task due to: (1) the HCC information on NCMRI is
invisible or insufficient makes extraction of liver tumors feature difficult;
(2) diverse imaging characteristics in multi-modality NCMRI causes feature
fusion and selection difficult; (3) no specific information between hemangioma
and HCC on NCMRI cause liver tumors detection difficult. In this study, we
propose a united adversarial learning framework (UAL) for simultaneous liver
tumors segmentation and detection using multi-modality NCMRI. The UAL first
utilizes a multi-view aware encoder to extract multi-modality NCMRI information
for liver tumor segmentation and detection. In this encoder, a novel edge
dissimilarity feature pyramid module is designed to facilitate the
complementary multi-modality feature extraction. Second, the newly designed
fusion and selection channel is used to fuse the multi-modality feature and
make the decision of the feature selection. Then, the proposed mechanism of
coordinate sharing with padding integrates the multi-task of segmentation and
detection so that it enables multi-task to perform united adversarial learning
in one discriminator. Lastly, an innovative multi-phase radiomics guided
discriminator exploits the clear and specific tumor information to improve the
multi-task performance via the adversarial learning strategy. The UAL is
validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI,
T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical
subjects. The experiments show that UAL has great potential in the clinical
diagnosis of liver tumors.
