## 4 Experiments
### Layer Segmentation
#### 4.2.3 Comparative Methods and Metric
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Architecture** & **Params** & \(\mathbf{X_{0,0}}/\mathbf{X_{0,4}}\) & \(\mathbf{X_{1,0}}/\mathbf{X_{1,3}}\) & \(\mathbf{X_{2,0}}/\mathbf{X_{2,2}}\) & \(\mathbf{X_{3,0}}/\mathbf{X_{3,1}}\) & \(\mathbf{X_{4,0}}/\mathbf{X_{4,0}}\) \\ \hline U-Net [10] & 13.39M & 64 & 128 & 256 & 512 & 1024 \\ ReLayNet [17] & 7.74M & 64 & 64 & 64 & 64 & 64 \\ CE-Net [18] & 29M & - & - & - & - & - \\ UNet++ [21] & 9.16M & 32 & 64 & 128 & 256 & 512 \\ MDAN-UNet-16 & 3.77M & 16 & 32 & 64 & 128 & 256 \\ MDAN-UNet-32 & 15.02M & 32 & 64 & 128 & 256 & 512 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the number of networks’ parameters and convolutional kernels. \(\mathbf{X_{i,j}}/\mathbf{X_{i,k}}\) in column headers refer to encoder/decoder and the values in those columns denote the number of convolutional kernels. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **ILM** & **NFL-IPL** & **INL** & **OPL** & **ONL-ISM** & **ISE** & **OSE-RPE** & **Average** \\ \hline \multirow{4}{*}{Expert 1} & LSE [7] & 1.764 & 2.25 & 2.195 & 2.315 & 2.314 & 1.268 & 1.231 & 1.905 \\  & U-Net [10] & 1.542 & 1.763 & 1.936 & 1.732 & 2.126 & 1.149 & * 1.037 & 1.612 \\  & ReLayNet [17] & 1.558 & 1.894 & 1.802 & **1.699** & 2.165 & 1.178 & **1.032** & 1.618 \\  & CE-Net [18] & 1.567 & 1.852 & **1.638** & 1.745 & 2.039 & 1.234 & 1.104 & 1.597 \\  & UNet++ [21] & 1.533 & 1.887 & 1.733 & 1.743 & * 1.952 & **1.060** & 1.111 & 1.574 \\  & MDAN-UNet-16 & **1.466** & * 1.728 & 1.661 & * 1.701 & 2.006 & 1.112 & 1.092 & * 1.538 \\  & MDAN-UNet-32 & * 1.480 & **1.686** & * 1.640 & 1.710 & **1.928** & * 1.099 & 1.055 & **1.514** \\ \hline \multirow{4}{*}{Expert2} & LSE [7] & 2.055 & 2.533 & 2.264 & 2.25 & 2.303 & 1.327 & 1.429 & 2.023 \\  & U-Net [10] & * 1.891 & * 2.117 & 2.010 & 1.860 & 2.129 & 1.318 & 1.347 & 1.810 \\ \cline{1-1}  & ReLayNet [17] & 2.028 & 2.273 & 1.900 & **1.732** & 2.160 & 1.374 & 1.319 & 1.827 \\ \cline{1-1}  & CE-Net [18] & 1.920 & 2.192. & **1.835** & 1.833 & * 1.978 & 1.393 & 1.349 & 1.786 \\ \cline{1-1}  & U-Net++ [21] & 1.931 & 2.126 & 1.932 & 1.803 & 2.030 & 1.286 & **1.284** & 1.767 \\ \cline{1-1}  & MDAN-UNet-16 & **1.881** & 2.128 & 1.905 & * 1.778 & 2.054 & * 1.277 & * 1.298 & * 1.760 \\ \cline{1-1}  & MDAN-UNet-32 & * 1.891 & **2.110** & * 1.869 & 1.816 & **1.959** & **1.257** & 1.311 & **1.745** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thickness error(TE) for each layer: performance comparison of layer segmentation. The best one shown in **bold** type and **’*’ marks the second best. We report the number of parameters and convolutional kernels of models given in Table 1. We observe that the number of parameters of MDAN-UNet-16, which is 3.77M, is much less than others. Additionally, ReLayNet [17], with 7.74 M parameters, has the second smallest parameters among them. MDAN-UNet-32 has about 6M more parameters than UNet++ [21] and about 1.6M more parameters than U-Net [10]. Moreover, CE-Net [18] has the largest parameters. The dice score of each experiment for every epoch is illustrated in Figure 5. As we can see, all the methods reach an average DSC of 0.8 in the first 20 epochs and achieve stable DSC after 80 epochs. In addition, the DSC for MDAN-UNet-32 increases at a faster rate compared with other methods in the first 10 epochs, and the rapid increase in DSC for MDAN-UNet-32 may be attributed to the multi-scale side output which can enhance deep supervision and help early layer training. The DSC for MDAN-UNet-32, MDAN-UNet-16 and CE-Net [18] maintain a relatively stable growth after 15 epochs, while the DSC for U-Net [10], UNet++ [21] and ReLayNet [17] fluctuates.