  Brain tumor segmentation remains a challenge in medical image segmentation
tasks. With the application of transformer in various computer vision tasks,
transformer blocks show the capability of learning long-distance dependency in
global space, which is complementary with CNNs. In this paper, we proposed a
novel transformer-based generative adversarial network to automatically segment
brain tumors with multi-modalities MRI. Our architecture consists of a
generator and a discriminator, which are trained in min-max game progress. The
generator is based on a typical "U-shaped" encoder-decoder architecture, whose
bottom layer is composed of transformer blocks with resnet. Besides, the
generator is trained with deep supervision technology. The discriminator we
designed is a CNN-based network with multi-scale $L_{1}$ loss, which is proved
to be effective for medical semantic image segmentation. To validate the
effectiveness of our method, we conducted experiments on BRATS2015 dataset,
achieving comparable or better performance than previous state-of-the-art
methods.
