  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.
