Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
This work addresses myocardium infarction segmentation in medical imaging, offering an incremental improvement by combining style transfer and self-supervised learning for enhanced accuracy.
The study tackled the problem of segmenting myocardium infarction in low-resolution diffusion tensor imaging by translating images to a high-resolution domain and using a super-resolution segmentation model, achieving improved segmentation performance through self-supervised pre-training.
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