YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation
Tom Liu Hui Lin Aggelos K. Katsaggelos Adrienne Kline
Coronary angiography remains the gold standard for diagnosis of coronary
artery disease, the most common cause of death worldwide. While this procedure
is performed more than 2 million times annually, there remain few methods for
fast and accurate automated measurement of disease and localization of coronary
anatomy. Here, we present our solution to the Automatic Region-based Coronary
Artery Disease diagnostics using X-ray angiography images (ARCADE) challenge
held at MICCAI 2023. For the artery segmentation task, our three-stage approach
combines preprocessing and feature selection by classical computer vision to
enhance vessel contrast, followed by an ensemble model based on YOLOv8 to
propose possible vessel candidates by generating a vessel map. A final
segmentation is based on a logic-based approach to reconstruct the coronary
tree in a graph-based sorting method. Our entry to the ARCADE challenge placed
3rd overall. Using the official metric for evaluation, we achieved an F1 score
of 0.422 and 0.4289 on the validation and hold-out sets respectively.