Several studies have shown the excellent performance of deep learning in image segmentation. Usually, this benefits from a large amount of annotated data. Medical image segmentation is challenging, however, since there is always a scarcity of annotated data. This study constructs a novel deep network for medical image segmentation, referred to as asymmetric U-Net generative adversarial networks with multi-discriminators (AU-MultiGAN). Specifically, the asymmetric U-Net is designed to produce multiple segmentation maps simultaneously and use the dual-dilated blocks in the feature extraction stage only. Further, the multi-discriminator module is embedded into the asymmetric U-Net structure, which can capture the available information of samples sufficiently and thereby promote the information transmission of features. A hybrid loss by the combination of segmentation and discriminator losses is developed, and an adaptive method of selecting the scale factors is devised for this new loss. More importantly, the convergence of the proposed model is proved mathematically. The proposed AU-MultiGAN approach is implemented on some standard medical image benchmarks. Experimental results show that the proposed architecture can be successfully applied to medical image segmentation, and obtain superior performance in comparison with the state-of-the-art baselines.

多项研究表明深度学习在图像分割方面的出色表现。通常，这得益于大量带注释的数据。但是，由于始终缺少注释数据，因此医学图像分割具有挑战性。这项研究构建了一种新颖的用于医学图像分割的深度网络，称为具有多个鉴别器的非对称U-Net生成对抗网络（AU-MultiGAN）。具体来说，非对称U-Net被设计为同时生成多个分割图，并且仅在特征提取阶段使用双膨胀块。此外，多鉴别器模块被嵌入到不对称的U-Net结构中，该结构可以充分捕获样本的可用信息，从而促进特征的信息传递。通过分割损失和鉴别损失的组合，发展了一种混合损失，并针对这种新损失设计了一种选择比例因子的自适应方法。更重要的是，该数学模型的收敛性得到了数学证明。所提出的AU-MultiGAN方法是在一些标准医学图像基准上实现的。实验结果表明，所提出的体系结构可以成功地应用于医学图像分割，并且与最新的基线相比具有更好的性能。