# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 1 Introduction  
Magnetic resonance (MR) vessel wall imaging (VWI) has been proven to visualize plaques and quantitatively analyze their morphologic and signal characteristics [1]. One of the potential important applications of quantitative morphologic and signal measurements of the arterial vessel wall and plaques is to monitor intracranial atherosclerotic disease progression and regression during medical management, which can greatly assist a comprehensive study of atherosclerosis. Quantitative morphologic measurements require the segmentation of the arterial vessel wall [2]. However, the manual segmentation method can only be performed slice by slice based on the 2D images, which is inefficient and costly, and heavily depends on expert knowledge and experience. Therefore, a fast and precise computer-aided automatic segmentation method is potentially needed. Most previous studies were based on semi-automatic method. [PERSON] et al. proposed a Hough transform-based boundary identification method to automatically detect the carotid artery boundary in MR images. The method can realize the analysis of a tremendous number of images and all results are repeatable [3]. [PERSON] et al. presented a modified traditional level set method with a double adaptive threshold (DATLS) to semi-automatically segment the carotid artery on TOFMRA images, which performed well on images with a weak boundary and outperformed traditional level sets in terms of segmentation computing time, robustness, and accuracy [4]. [PERSON] et al. utilized active contours andknowledge-based methodology to segment and reconstruct the lumen and vessel based on the axial TOFMRA and T1W images of the carotid arteries and perform semi-automatic characterization of the plaque type, which can realize the fast and accurate characterization of the plaque type in the carotid MRI images [5]. However, these methods still need extensive manual input, such as seed points initialization, interested artery identification, and registration of image sequences. Recently, deep learning technology has been applied in arterial vessel and vessel wall segmentation because of its ability to use fully automated methods to solve complex and labor-intensive problems [6; 7; 8]. These methods utilize the superior learning capability of the neural network and outperform other traditional segmentation methods. [PERSON] et al. developed a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the vessel wall of the carotid artery, which automatically segmented the lumen and the outer wall together and achieved promising segmentation performances [9]. [PERSON] et al. proposed a fully automated tracking-by-detection approach to detect artery locations and segment the vessel wall in the polar coordinate system, which achieved a high accuracy [10]. [PERSON] et al. presented a multi-task regression in a polar coordinate system method for carotid artery vessel wall segmentation in black-blood MRI using anatomical priors; the results show that it is feasible to automatically obtain anatomically plausible segmentations of the carotid vessel wall with high accuracy [11]. [PERSON] et al. developed an intracranial vessel wall segmentation method with deep learning using a novel tiered loss function to incorporate class inclusion [12]. However, the arterial vessel wall segmentation based on the MR vessel wall image is still a challenging task due to the relatively low signal-to-noise ratio and contrast between vessel walls and surrounding tissues, which often lead to blurred boundaries and are harder to identify [13]. Specifically, edge information plays a critical role in biomedical image quantification as well as precise segmentation because it often carries abundant physiological information. However, the color, texture, and shapes are processed together by these previous CNN approaches. The diverse type of information related to recognition may lead to unsatisfying results.