Multimodal magnetic resonance imaging (MRI) can reveal different patterns of human tissue and is crucial for clinical diagnosis. However, limited by cost, noise and manual labeling, obtaining diverse and reliable multimodal MR images remains a challenge. For the same lesion, different MRI manifestations have great differences in background information, coarse positioning and fine structure. In order to obtain better generation and segmentation performance, a coordination-spatial attention generation adversarial network (CASP-GAN) based on the cycle-consistent generative adversarial network (CycleGAN) is proposed. The performance of the generator is optimized by introducing the Coordinate Attention (CA) module and the Spatial Attention (SA) module. The two modules can make full use of the captured location information, accurately locating the interested region, and enhancing the generator model network structure. The ability to extract the structure information and the detailed information of the original medical image can help generate the desired image with higher quality. There exist some problems in the original CycleGAN that the training time is long, the parameter amount is too large, and it is difficult to converge. In response to this problem, we introduce the Coordinate Attention (CA) module to replace the Res Block to reduce the number of parameters, and cooperate with the spatial information extraction network above to strengthen the information extraction ability. On the basis of CASP-GAN, an attentional generative cross-modality segmentation (AGCMS) method is further proposed. This method inputs the modalities generated by CASP-GAN and the real modalities into the segmentation network for brain tumor segmentation. Experimental results show that CASP-GAN outperforms CycleGAN and some state-of-the-art methods in PSNR, SSMI and RMSE in most tasks.

翻译：多模态磁共振成像（MRI）能够揭示人体组织的不同模式，对于临床诊断至关重要。然而，受限于成本、噪声和人工标注，获取多样且可靠的多模态MR图像仍是一大挑战。对于同一病灶，不同MRI表现中背景信息、粗定位和精细结构存在显著差异。为了获得更优的生成与分割性能，本文提出了一种基于循环一致性生成对抗网络（CycleGAN）的协调-空间注意力生成对抗网络（CASP-GAN）。通过引入坐标注意力（CA）模块和空间注意力（SA）模块优化生成器性能，这两个模块能够充分利用捕获的位置信息，精确定位感兴趣区域，并增强生成器模型网络结构。其提取原始医学图像结构信息与细节信息的能力有助于生成更高质量的期望图像。原始CycleGAN存在训练时间长、参数量过大及难以收敛等问题。针对这一问题，我们引入坐标注意力（CA）模块替代残差块以减少参数量，并与上述空间信息提取网络协同强化信息提取能力。在CASP-GAN基础上，进一步提出注意力生成跨模态分割（AGCMS）方法。该方法将CASP-GAN生成的模态与真实模态输入分割网络进行脑肿瘤分割。实验结果表明，在大多数任务中，CASP-GAN在PSNR、SSMI和RMSE指标上优于CycleGAN及若干最新方法。