Artery Vein Segmentation

Artery-vein segmentation aims to automatically distinguish arteries from veins in medical images, crucial for accurate diagnosis and treatment planning in various vascular diseases. Current research focuses on improving segmentation accuracy using deep learning models, often incorporating advanced architectures like U-Net variations and incorporating additional data modalities such as force sensing from ultrasound probes or leveraging motion information from robotic sonography to enhance performance. These advancements are improving the reliability of automated analysis in applications ranging from pulmonary disease assessment to robotic-assisted surgery, ultimately leading to more precise and efficient clinical workflows.