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

arXiv 2309.15485 ↗First posted 27 Sep 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 27 Sep 2023.

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12 of 29 references.

References · 12

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  10. 2023BiFormer: Vision Transformer with Bi-Level Routing Attentioncited by 151
  11. 2019Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Networkcited by 7
  12. 2022UNet-2022: Exploring Dynamics in Non-isomorphic Architecturecited by 6

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