MyoPS (Myocardial Pathology Segmentation) is used for auxiliary diagnosis of myocardial infarction by accurately segmenting myocardial lesions (such as scars and edema). However, CMR images are complex, manual segmentation is time-consuming and relies on professional knowledge, and there are differences in imaging data from different centers, which increases the difficulty of segmentation. To this end, this study developed a domain generalization module that flexibly integrates LGE, T2-weighted, and Cine sequences to improve cross-center and multi-sequence adaptability and robustness. Our method combines the domain generalization module with the nnUNet segmentation network, and reduces the differences between different data distributions by utilizing the domain generalization module for data mixing enhancement, thereby enhancing the model’s generalization ability and improving segmentation performance. In tests conducted on the data set of the MyoPS++ Challenge, our network performed well in segmenting scars and edema. Compared with the native segmentation network, it has a greater performance improvement, which verifies its ability to handle multi-center, Effectiveness in multi-sequence CMR data.