Atrial Segmentation

Atrial segmentation, the automated identification of the left and right atria in medical images (primarily MRI), is crucial for diagnosing and treating atrial fibrillation. Current research focuses on improving segmentation accuracy using various deep learning architectures, including U-Net variations, ResNets, and ensemble methods, often incorporating techniques like semi-supervised learning to leverage both labeled and unlabeled data and addressing challenges posed by multi-center data variability. These advancements aim to enhance the precision of atrial fibrillation diagnosis and treatment planning, ultimately improving patient outcomes.