# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 3 Results  
Figure 6 shows the segmentation results by the proposed ESegNet from the test dataset as well as their corresponding original image, mask, binary mask, and edgemap. Edgemaps are generated online by computing the Canny filter. As can be seen from Figure 6, our examples included multiscale vessel walls and low-contrast images. The compared results segmentation outputs with different backbone architecture, U-net (None), U-net (Res), U-net (DUC), U-net (DUC + HDC), and EVSegNet, respectively, and are illustrated in Table 1. Table 1 shows the comparison results of the four metrics in our method with different components of our collected dataset. As shown in Table 1, the baseline U-net achieved a Dice, accuracy, recall, and precision score of 81.0, 96.5, 78.6, and 88.4, respectively, for the vessel wall. The performance kept improving when successively adding Resnet, DUC, HDC, and edgestream modules. As a result, our finalized network included DUC, HDC, and edgestream for image segmentation. Compared to the other backbone architecture, our EVSegNet has larger Dice, accuracy, the most stable recall, and great precision scores. For the test dataset, EVSegNet achieved the best performance with a Dice of 87.5 for the vessel wall, followed by U-net (None), U-net (Res), U-net (DUC), U-net (DUC + HDC), achieving a Dice of 81.0, 84.5, 85.6, and 86.5, respectively. Figure 7 shows the violin plot of segmentation performance evaluation of different methods. As can be seen from the figure, the wider violin shape regions represent more aggregated values than those in the narrower zones. EVSegNet aggregates places with values larger than those aggregated by other methods. 85.6, 97.3, 85.2, and 90.9, respectively. The results indicated that the DUC block is beneficial, which can help promote the model's performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **U-Net (None)** & **U-Net (Res)** & **U-Net (DUC)** & **U-Net (DUC + HDC)** & **EVSegNet** \\ \hline Dice coefficient \% & 81.0 (2.5) & 84.5 (1.2) & 85.6 (1.4) & 86.5 (1.2) & 87.5 (1.4) \\ Accuracy \% & 96.5 (0.3) & 96.8 (0.3) & 97.3 (0.4) & 97.4 (0.4) & 97.6 (0.3) \\ Recall \% & 78.6 (2.3) & 83.8 (1.7) & 85.2 (1.7) & 86.1 (1.7) & 86.8 (1.7) \\ Precision \% & 88.4 (2.9) & 89.0 (1.7) & 90.9 (1.5) & 91.3 (1.2) & 91.5 (1.1) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Comparison of Segmentation Results of Different Backbone Architecture. Figure 6: Illustration of segmentation results of VWI dataset. The first column represents the original image, the second column represents the mask, the third column is binary mas, the fourth represents the edgemaps, which are generated by using Canny filter, and the last column is the corresponding results of the prediction. Figure 7: Violin plot of segmentation performance evaluation on different methods. Dashed line shows the 25 th and 75 th percentiles. The solid line represents the mean value. In this work, we evaluated the U-net (Res), U-net (DUC), U-net (DUC + HDC), and EVSegNet. Ablation study for HDC block: We employed the HDC block with different dilation rates to replace the original bottleneck block of U-Net aiming at extracting multiscale features from the MR vessel wall images. As depicted in Table 1, our model performance can be improved by adopting HDC. Our proposed HDC module (referred to U-Net + DUC) improves the Dice, accuracy, recall, and precision scores are increased from 85.6, 97.3, 85.2, and 90.9 to 86.5, 97.4, 86.1, and 91.3, respectively. Figure 8 shows how the Dice curves change of 11 epochs for different models for the test dataset. From Figure 8, it can be seen that the HDC model has a higher convergence speed than the traditional CNN model in terms the performance index of Dice and has a better performance. We validate that HDC contributes to the performance of the model and accelerate convergence. Ablation study for edge stream: Motivated by [PERSON] et al.