Myocardial Pathology Segmentation

Myocardial pathology segmentation aims to automatically identify and delineate different regions of the heart muscle (myocardium), such as healthy tissue, scar tissue, and edema, from medical images like cardiac magnetic resonance (CMR) and echocardiography. Current research heavily utilizes deep learning, employing architectures like U-Net, Res-UNet, nnU-Net, and Vision Transformers, often incorporating multi-sequence image fusion and techniques like shape modeling and attention mechanisms to improve accuracy and robustness. Accurate and automated segmentation is crucial for improved diagnosis and treatment planning of cardiovascular diseases like myocardial infarction, enabling personalized therapies and risk stratification.