Cardiac Segmentation

Cardiac segmentation, the automated identification of heart structures in medical images, aims to improve the efficiency and accuracy of cardiovascular disease diagnosis. Current research emphasizes developing robust and efficient deep learning models, including U-Net variations, Transformers, and graph convolutional networks, often addressing challenges like limited annotated data through techniques such as semi-supervised learning, weak supervision (e.g., scribbles), and unsupervised domain adaptation. These advancements hold significant potential for improving diagnostic accuracy, streamlining clinical workflows, and enabling personalized treatment planning.