This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor imaging (DTI) images by translating them into the late gadolinium enhancement (LGE) domain, which offers a larger amount of data with high-resolution and distinct highlighting of myocardium infarction (MI) areas. Subsequently, the segmentation task is performed on the LGE style image. An end-to-end super-resolution segmentation model is introduced to generate high-resolution mask from low-resolution LGE style DTI image. Further, to enhance the performance of the model, a multi-task self-supervised learning strategy is employed to pre-train the super-resolution segmentation model, allowing it to acquire more representative knowledge and improve its segmentation performance after fine-tuning. https: github.com/wlc2424762917/Med_Img

翻译：本研究提出了一种融合新型风格迁移模型与同步超分辨率分割模型的流水线。该流水线旨在通过将扩散张量成像（DTI）图像转换为晚期钆增强（LGE）图像域，从而增强DTI图像——LGE域提供了大量具有高分辨率且能清晰凸显心肌梗死（MI）区域的数据。随后，在LGE风格的图像上执行分割任务。引入了一种端到端的超分辨率分割模型，用于从低分辨率LGE风格DTI图像生成高分辨率掩膜。此外，为提升模型性能，采用了一种多任务自监督学习策略对超分辨率分割模型进行预训练，使其能够获取更具代表性的知识，并在微调后改善分割性能。https: github.com/wlc2424762917/Med_Img