X線冠状動脈分岐インスタンスセグメンテーションおよびマッチングタスクに関する研究

In the task of 3D reconstruction of X-ray coronary artery, matching vessel branches in different viewpoints is a challenging task. In this study, this task is transformed into the process of vessel branches instance segmentation and then matching branches of the same color, and an instance segmentation network (YOLO-CAVBIS) is proposed specifically for deformed and dynamic vessels. Firstly, since the left and right coronary artery branches are not easy to distinguish, a coronary artery classification dataset is produced and the left and right coronary artery arteries are classified using the YOLOv8-cls classification model, and then the classified images are fed into two parallel YOLO-CAVBIS networks for coronary artery branches instance segmentation. Finally, the branches with the same color of branches in different viewpoints are matched. The experimental results show that the accuracy of the coronary artery classification model can reach 100%, and the mAP50 of the proposed left coronary branches instance segmentation model reaches 98.4%, and the mAP50 of the proposed right coronary branches instance segmentation model reaches 99.4%. In terms of extracting deformation and dynamic vascular features, our proposed YOLO-CAVBIS network demonstrates greater specificity and superiority compared to other instance segmentation networks, and can be used as a baseline model for the task of coronary artery branches instance segmentation. Code repository: https://gitee.com/zaleman/ca_instance_segmentation, https://github.com/zaleman/ca_instance_segmentation.