Unsupervised unpaired multiple fusion adaptation aided with self-attention generative adversarial network for scar tissues segmentation framework

Abstract

Late gadolinium enhancement (LGE) is a specialized imaging technique used in cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar tissue or fibrosis within the heart muscle. After a heart attack, the affected region of the heart muscle becomes scarred due to insufficient blood supply. LGE CMR enhances the infarcted myocardium to appear with distinctive brightness compared to healthy tissues. It is primarily used to assess various heart conditions and can identify the extent and location of scar tissue, helping in risk stratification and guiding treatment decisions. Automated myocardium segmentation in LGE CMR poses challenges due to intensity heterogeneity caused by the accumulation of contrast agents in infarcted areas. Furthermore, LGE CMR images with gold standard labels are particularly scarce compared to other sequences, making the task even more demanding. Besides, the heart’s motion involves complex non-rigid deformations, including regional wall thickening, circumferential, and longitudinal ventricular shortening, making it challenging to model. This work presents an unsupervised unpaired domain adaptive scar tissue segmentation framework aided with the self-attention generative adversarial network. Besides, we proposed a novel segmentation architecture based on a parallel transformer for scar segmentation and compared the results with and without style transfer GAN and unpaired style self-supervised attention GAN. Besides, to model longitudinal changes in scare, we used eight days’ time points as a source and 1 month and 12 as a target and applied our proposed attention-generative network to transfer style from one-time point to another time. Experiments on four different LGE MICCAI datasets from 2019 to 2023 achieved significantly better performance than state-of-the-art segmentation methods.