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
@article{arxiv.2309.15485,
title = {Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation},
author = {Lichao Wang and Jiahao Huang and Xiaodan Xing and Yinzhe Wu and Ramyah Rajakulasingam and Andrew D. Scott and Pedro F Ferreira and Ranil De Silva and Sonia Nielles-Vallespin and Guang Yang},
journal= {arXiv preprint arXiv:2309.15485},
year = {2023}
}
Comments
6 pages, 8 figures, conference, accepted by SIPAIM2023