Vessel Wall

Vessel wall research focuses on accurately segmenting and analyzing the vessel wall in medical images, primarily to improve diagnosis and treatment of vascular diseases. Current research employs deep learning models, particularly U-Net architectures and novel boundary-delineation networks, often incorporating techniques like multi-scale feature extraction and anatomical priors to overcome challenges such as discontinuous boundaries and class imbalance in image data. These advancements enable more precise quantification of vessel wall characteristics, leading to improved diagnostic accuracy and potentially personalized treatment strategies for conditions like atherosclerosis and varicose veins.