Style Transfer and Self-Supervised Learning Powered Myocardium
  Infarction Super-Resolution Segmentation
Lichao Wang Jiahao Huang Xiaodan Xing Yinzhe Wu Ramyah Rajakulasingam Andrew D. Scott Pedro F Ferreira Ranil De Silva Sonia Nielles-Vallespin Guang Yang
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