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：多模态磁共振成像能够揭示人体组织的不同成像模式，对临床诊断至关重要。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图像仍面临挑战。针对同一病灶，不同MRI表现存在背景信息、粗定位和精细结构方面的显著差异。为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模块和空间注意力模块优化生成器性能：前者可充分利用捕获的位置信息精确定位感兴趣区域，后者能增强生成器模型网络结构对原始医学图像结构信息与细节信息的提取能力，从而生成更高质量的期望图像。针对原始CycleGAN存在的训练周期长、参数量过大及收敛困难等问题，我们引入坐标注意力模块替代残差块以降低参数量，并与上文的空间信息提取网络协同强化信息提取能力。在CASP-GAN基础上进一步提出了注意力生成跨模态分割方法，将CASP-GAN生成的模态与真实模态共同输入分割网络进行脑肿瘤分割。实验结果表明，在多数任务中，CASP-GAN在PSNR、SSIM和RMSE指标上均优于CycleGAN及当前最先进方法。