论文标题
联合对抗性学习用于肝肿瘤分割和多模式的检测非对比度MRI
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI
论文作者
论文摘要
通过使用多模式的非对比度磁共振成像（NCMRI）的同时分割和检测肝肿瘤（血管瘤和肝细胞癌（HCC））对于临床诊断至关重要。但是，由于：（1）关于NCMRI的HCC信息是看不见的，或者不足，因此提取肝肿瘤的特征很困难； （2）多模式NCMRI中的各种成像特性导致融合和选择困难； （3）血管瘤和HCC之间关于NCMRI的特定信息引起肝肿瘤的检测困难。在这项研究中，我们提出了一个联合对抗性学习框架（UAL），用于使用多模式NCMRI同时进行肝肿瘤分割和检测。 UAL首先利用多视图的编码器来提取多模式NCMRI信息以进行肝肿瘤分割和检测。在此编码器中，一种新型的边缘差异特征金字塔模块旨在促进互补的多模式特征提取。其次，新设计的融合和选择通道用于融合多模式的功能并做出功能选择的决定。然后，提出的与填充协调共享的机制集成了分割和检测的多任务，从而使多任务可以在一个歧视者中执行联合对抗性学习。最后，创新的多相放射素学指导歧视者利用清晰而特定的肿瘤信息，通过对抗性学习策略提高多任务性能。 UAL在相应的多模式NCMRI（即T1FS Pre-Contcontrast MRI，T2FS MRI和DWI）中得到了验证，以及255名临床受试者的三个阶段对比增强的MRI。实验表明，UAL在肝肿瘤的临床诊断中具有巨大的潜力。
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.
