## 4 Experiments
### Layer Segmentation
#### 4.2.3 Comparative Methods and Metric
The DSC for MDAN-UNet is more stable than the DSC for U-Net [10], UNet++ [21] and ReLayNet [17], and the possible reasons can be listed as follows: in the first place, MDAN-UNet takes advantages of multi-scale input which could achieve multiple level sizes of receptive field; secondly, MDAN-UNet utilizes multi-scale side output and multi-scale label which could help layer training correctly; moreover, MDAN-UNet applies dual attention block to capture global information in a spatial and channel dimension. From the comparison shown in Tables 2-4, MDAN-UNet-32 achieves best performance to achieve the highest average dice score: 0.883 for expert 1 annotation and 0.866 for expert 2 annotation, lowest \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **Contour1** & **Contour2** & **Contour3** & **Contour4** & **Contour5** & **Contour6** & **Contour7** & **Contour8** & **Average** \\ \hline \multirow{8}{*}{Expert 1} & LSE [7] & **0.969** & 1.625 & 1.698 & 1.704 & 2.146 & 0.863 & 1.086 & 0.863 & 1.369 \\  & U-Net [10] & 1.046 & 1.455 & 1.450 & 1.788 & 1.997 & 0.796 & 1.490 & 0.936 & 1.369 \\  & ReLayNet [17] & 1.024 & 1.523 & 1.530 & 1.811 & 1.886 & 0.906 & \(+\)0.902 & **0.796** & 1.297 \\  & CE-Net [18] & 0.996 & 1.448 & 1.445 & \({}^{*}\)1.547 & **1.764** & 0.963 & 0.971 & 0.943 & 1.260 \\  & UNet++ [21] & \({}^{*}\)0.981 & 1.377 & 1.495 & 1.595 & 1.840 & 0.833 & **0.896** & 0.931 & 1.244 \\  & MDAN-UNet-16 & 0.995 & **1.334** & \({}^{*}\)1.357 & 1.603 & \(+\)1.802 & \(+\)0.779 & 0.560 & 0.872 & \({}^{*}\)1.212 \\  & MDAN-UNet-32 & 1.040 & \({}^{*}\)1.336 & **1.323** & **1.493** & 1.825 & 0.777 & 0.919 & \({}^{*}\)0.832 & **1.193** \\ \hline \multirow{8}{*}{Expert2} & LSE [7] & \({}^{*}\)0.906 & 1.826 & 1.853 & 1.753 & 2.125 & 0.901 & 1.229 & 1.112 & 1.463 \\  & U-Net [10] & 1.026 & 1.721 & 1.623 & 1.887 & 2.089 & 0.865 & 1.782 & 1.170 & 1.521 \\  & ReLayNet [17] & **0.965** & 1.865 & 1.739 & 1.892 & 1.930 & 0.842 & 1.157 & \({}^{*}\)1.070 & 1.432 \\  & CE-Net [18] & 0.966 & 1.740 & 1.605 & 1.678 & **1.810** & 0.911 & 1.203 & 1.186 & 1.387 \\  & UNet++ [21] & 0.968 & 1.733 & 1.634 & 1.812 & \({}^{*}\)1.884 & **0.824** & **1.110** & 1.187 & 1.394 \\  & MDAN-UNet-16 & 0.926 & **1.671** & \({}^{*}\)1.534 & 1.769 & 1.887 & \({}^{*}\)0.839 & 1.152 & 1.102 & \({}^{*}\)1.364 \\  & MDAN-UNet-32 & 1.043 & \({}^{*}\)1.690 & **1.514** & **1.641** & 1.928 & 0.867 & \({}^{*}\)1.148 & **1.064** & **1.362** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean contour error(CE): performance comparison of layer segmentation. The best one shown in **bold** type and \({}^{**}\) marks the second best. Figure 5: Evolution of test dice score (DSC) when training on Duke publicly available dataset. The DSC is the average of DSC for expert 1 annotation and expert 2 annotation. average thickness error(TE): 1.514 and 1.745 for two experts' annotation respectively, and lowest average contour error: 1.193 and 1.362 for two experts' annotation respectively. From the aspect of average dice score, average thickness error and average contour error, MDAN-UNet-16 achieves second best performance, followed by U-Net++, CE-Net, U-Net, ReLayNet and LSE in sequence. With the smallest parameter, MDAN-UNet-16 shows promising performance and outperforms U-Net++, CE-Net, ReLayNet and LSE in layer segmentation. From the aspect of average dice score and average thickness error, all fully convolution networks outperform LSE, the traditional segmentation method. When it comes to contour error(CE), LSE achieves an average of 1.369 for expert 1 annotation, the same as U-Net, and achieve an average of 1.463 for expert 2 annotation which is better than U-Net with an average of 1.521.