Accurate segmentation of brain tumors is essential in the planning of neurosurgical treatments as it can significantly enhance their effectiveness. In this paper, the authors propose a modified Residual U-shaped network (ResUnet) based on multimodal fusion and a Generative Adversarial Network for multimodal brain tumor Magnetic Resonance Imaging segmentation. First, they propose a three-path structure for the encoding stage to address the issue of inadequate utilization of multimodal features, which leads to suboptimal segmentation results. The structure comprises three components: the T1 path, the T1ce path, and the fusion path combining Flair and T2 modalities. They then utilize average pooling to integrate the global information from the T1 path into both the T1ce and the fusion path, enhancing the feature fusion across different modalities and strengthening the robustness of the network. Subsequently, the features from the T1ce path and the fusion path are connected to the decoding stage through skip connections to enhance the utilization of model features and improve segmentation accuracy. Finally, the authors leverage the Deep Convolutional Generative Adversarial Network (DCGAN) to further enhance the accuracy of the network. They improve the loss function of the DCGAN by introducing an adaptive coefficient, which reduces the loss value in the early stages of model training and increases it in the later stages. Experimental results demonstrate that the proposed method effectively improves segmentation accuracy compared to related methods.