Direction- and Centerline-Aware Joint Learning Network (JLNet) for Vessel Segmentation in X-Ray Angiography Images

Vessel segmentation from X-ray angiography (XRA) images is an important task in the clinical diagnosis of coronary artery disease. The main challenge lies in how to extract continuous and completed vessel masks from XRA images with poor quality and high complexity. Previous state-of-the-art methods are mostly based on hand-crafted features or pixel-wise segmentation and ignore geometric features, thereby resulting in breaks and absence of vessel structure in segmentation masks. In this paper, we propose a geometric feature embedding segmentation network for vessel segmentation in XRA images. This network joins direction- and centerline-related prediction tasks with mask segmentation, which enforces the network to learn the geometric features of vessel connectivity. Besides, a novel joint loss function is proposed to facilitate the joint training of these three tasks. We conduct ablation experiments on XRA images to demonstrate that the two auxiliary tasks can improve the connectivity and completeness of vessel segmentation. We also evaluate our method on XRA images and achieve the value of 85.00±3.66% for vessel segmentation, indicating that our method outperforms the other state-of-the-art methods.