Multi-source domain generalization （DG） aims to train a model uses semantic information of different domains and can be generalized to unknown domains. In the medical image， the gap between different domains is relatively large， and the model will suffer from performance drop in the unknown domain. In order to solve this problem， this paper proposes a network structure which encodes images for features and decodes domain specific features. The model uses a generic encoder， which learns all source domains for the domain-invariant features， and several domain-specific decoders to reconstruct the original images to promote the ability of extracting image features. Meanwhile， these decoders also help to generate transferred image to engage in adversarial learning with images of source domains in order to improve the encoder’s ability of learning invariant features. In addition， we also introduce a special Cutmix strategy which change foreground information of different domain images to augment the data set in the model to enhance the generalization ability of the model and further improve the performance of our network structure. In two medical image segmentation tasks， a large number of experimental data show that the proposed model has excellent performance compared with the existing advanced models. In addition， a series of ablation experiments are carried out to prove the effectiveness of the model.