Plaque Segmentation

Plaque segmentation, the automated identification and delineation of plaque in medical images, aims to improve the diagnosis and management of cardiovascular diseases and other conditions like Alzheimer's. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), including U-Net and its variants, often incorporating recurrent neural networks (RNNs) for dynamic imaging analysis and weakly supervised learning techniques to reduce annotation burden. These advancements enable more accurate and efficient plaque quantification, leading to improved risk assessment, treatment planning, and potentially facilitating earlier interventions.