Echocardiography Segmentation

Echocardiography segmentation aims to automatically identify and delineate cardiac structures in ultrasound images, facilitating faster and more accurate diagnosis of cardiovascular diseases. Current research focuses on improving segmentation accuracy and robustness across diverse image qualities and viewpoints, employing techniques like reinforcement learning, prompt-driven universal models, and vision-language models alongside architectures such as U-Net, nnU-Net, and transformers. These advancements address challenges like domain adaptation, limited annotated data, and temporal consistency in video segmentation, ultimately aiming to create reliable automated tools for clinical use and improve the efficiency and accuracy of echocardiographic analysis.