Transformers-Based Neural Network for Cardiac Infarction Segmentation in Delayed-Enhancement MRI

Accurately and robustly segmenting myocardial infarction (MI) is crucial for clinical diagnosis of cardiac diseases, treatment and planning. In this study, we propose a novel deep learning model specifically designed for automatic segmentation of MI in Late Gadolinium Enhancement cardiac MRI (LGE-MRI). LGE-MRI is widely used in clinical practice to quantify MI and plays a vital role in treatment decisions. However, due to the presence of high anisotropy and inhomogeneities in LGE-MRI, accurately segmenting the infarcted tissue poses significant challenges.The first step of our approach is based on a U-net with resdiual to extract . By leveraging the power of transformer-based architectures, our model achieves competitive results. We evaluated our method on the 2020 MICCAI EMIDEC challenge dataset and obtained a dice score of 91.33% for myocardium segmentation and 74.41% for infarction segmentation …