Coronary Artery Segmentation

Coronary artery segmentation, the automated identification of coronary arteries in medical images like CT angiograms, aims to improve the diagnosis and treatment of coronary artery disease (CAD). Current research heavily utilizes deep learning, employing various architectures such as U-Net variations, transformers, and YOLO-based object detection, often incorporating attention mechanisms and feature aggregation techniques to overcome challenges posed by low contrast and complex anatomy. These advancements are crucial for improving the accuracy and efficiency of CAD diagnosis, potentially leading to better patient outcomes and reduced healthcare costs. The field is also actively addressing data limitations through techniques like data augmentation and semi-supervised learning.