A Semantic Segmentation Method with Emphasizing Edge information for Automatic Vessel Wall Analysis

Edge information is essential for medical image analysis, especially for image segmentation. This paper aims to develop a precise semantic segmentation method with emphasizing the edges for automated segmentation of arterial vessel wall and plaque based on the convolutional neural network (CNN) for facilitating the quantitative assessment of plaque in patients with ischemic stroke. An end-to-end architecture network that can emphasize the edge information is proposed. The results suggest that the proposed segmentation method improves segmentation accuracy effectively and will facilitate the quantitative assessment on atherosclerosis.