Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

TL;DR

This paper introduces a novel pipeline combining style transfer and self-supervised learning to improve myocardium infarction segmentation in diffusion tensor imaging by translating images into a high-resolution LGE domain and performing super-resolution segmentation.

Contribution

It presents a new end-to-end super-resolution segmentation model enhanced with multi-task self-supervised pre-training for better MI detection in DTI images.

Findings

Improved segmentation accuracy on MI areas.

Effective translation of DTI to LGE domain.

Enhanced model performance with self-supervised pre-training.

Abstract

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