Featured　Application　Cerebrovascular.　To　develop　a　precise　semantic　segmentation　method　with　an　emphasis　on　the　edges　for　automated　segmentation　of　the　arterial　vessel　wall　and　plaque　based　on　the　convolutional　neural　network　(CNN)　in　order　to　facilitate　the　quantitative　assessment　of　plaque　in　patients　with　ischemic　stroke.　A　total　of　124　subjects＇　MR　vessel　wall　images　were　used　to　train,　validate,　and　test　the　model　using　deep　learning.　An　end-to-end　architecture　network　that　can　emphasize　the　edge　information,　namely　the　Edge　Vessel　Segmentation　Network　(EVSegNet)　for　automated　segmentation　of　the　arterial　vessel　wall,　is　proposed.　The　EVSegNet　network　consists　of　two　workflows:　one　is　implemented　to　achieve　finely　and　multiscale　segmentation　by　combining　Dense　Upsampling　Convolution　(DUC)　and　Hybrid　Dilated　Convolution　(HDC)　with　different　dilation　rates　modules,　and　the　other　utilizes　edge　information　and　is　fused　with　another　workflow　to　finally　segment　the　vessel　wall.　The　proposed　network　demonstrates　robust　segmentation　of　the　vessel　wall　and　better　performance　with　a　Dice　(%)　of　87.5,　compared　with　the　traditional　U-net　that　has　a　Dice　(%)　of　81.0　and　other　U-net-based　models　on　the　test　dataset.　The　results　suggest　that　the　proposed　segmentation　method　with　an　emphasis　on　the　edges　improves　segmentation　accuracy　effectively　and　will　facilitate　the　quantitative　assessment　of　atherosclerosis.