Recursive centerline- and direction-aware joint learning network with ensemble strategy for vessel segmentation in x-ray angiography images
Abstract —Background and objective: Automatic vessel segmentation from X-ray angiography images is an important research topic for the diagnosis and treatment of cardiovascular disease. The main challenge is how to ex- tract continuous and completed vessel structures from XRA images with poor quality and high complex- ity. Most existing methods predominantly focus on pixel-wise segmentation and overlook the geometric features, resulting in breaking and absence in segmentation results. To improve the completeness and ac- curacy of vessel segmentation, we propose a recursive joint learning network embedded with geometric features. Methods: The network joins the centerline- and direction-aware auxiliary tasks with the primary task of segmentation, which guides the network to explore the geometric features of vessel connectivity. Moreover, the recursive learning strategy is designed by passing the previous segmentation result into the same network iteratively to improve segmentation. To further enhance connectivity, we present a complementary-task ensemble strategy by fusing the outputs of the three tasks for the final segmenta- tion result with majority voting. Results: To validate the effectiveness of our method, we conduct qualitative and quantitative experiments on the XRA images of the coronary artery and aorta including aortic arch, thoracic aorta, and abdominal aorta. Our method achieves F 1 scores of 85.61 ±3.48% for the coronary artery, 89.02 ±2.89% for the aortic arch, 88.22 ±3.33% for the thoracic aorta, and 83.12 ±4.61% for the abdominal aorta. Conclusions: Compared with six state-of-the-art methods, our method shows the most complete and accurate vessel segmentation results.
