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Simultaneous segmentation and detection of liver tumors (hemangioma andhepatocellular carcinoma (HCC)) by using multi-modality non-contrast magneticresonance imaging (NCMRI) are crucial for the clinical diagnosis. However, itis still a challenging task due to: (1) the HCC information on NCMRI isinvisible or insufficient makes extraction of liver tumors feature difficult;(2) diverse imaging characteristics in multi-modality NCMRI causes featurefusion and selection difficult; (3) no specific information between hemangiomaand HCC on NCMRI cause liver tumors detection difficult. In this study, wepropose a united adversarial learning framework (UAL) for simultaneous livertumors segmentation and detection using multi-modality NCMRI. The UAL firstutilizes a multi-view aware encoder to extract multi-modality NCMRI informationfor liver tumor segmentation and detection. In this encoder, a novel edgedissimilarity feature pyramid module is designed to facilitate thecomplementary multi-modality feature extraction. Second, the newly designedfusion and selection channel is used to fuse the multi-modality feature andmake the decision of the feature selection. Then, the proposed mechanism ofcoordinate sharing with padding integrates the multi-task of segmentation anddetection so that it enables multi-task to perform united adversarial learningin one discriminator. Lastly, an innovative multi-phase radiomics guideddiscriminator exploits the clear and specific tumor information to improve themulti-task performance via the adversarial learning strategy. The UAL isvalidated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI,T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinicalsubjects. The experiments show that UAL has great potential in the clinicaldiagnosis of liver tumors.

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI