## 4 Experiments
### Layer Segmentation
#### 4.2.3 Comparative Methods and Metric  
We compared our proposed method with some state-of-the-art OCT layer segmentation algorithms: learning layer specific edges method (LSE) [7], U-Net [10], ReLayNet [17], CE-Net [18] and U-Net++ with deep supervision [21]. Note that we did not run LSE [7] in out experiment and apply the original result in [7] due to the fact that the way we processed the data set is nearly identical to [7]. To evaluate the performance, we adopted three standard metrics as calculated in [6,7]. They are dice score (denote it as DSC), estimated contour error for each layer (denote it as CE) and estimated thickness error for each layer (denote it as TE). * Dice score, which has been commonly used to evaluate the overlap of OCT segmentation: \[DSC=\frac{2|P\bigcap Y|}{|P|+|Y|}\] where \(P\) and \(Y\) are predicted output and ground truth respectively. * Estimated contour error calculates mean absolute difference between the predicted layer contour and the ground truth layer contour along the column. The estimated contour error for contour \(c\) can be formulated as \[CE_{c}=\frac{\sum_{i}^{N}abs(p_{ci}-g_{ci})}{N}\] where \(p_{ci}\) and \(g_{ci}\) denote the predicted row location of contour \(c\) in column \(i\) and the ground truth one respectively, and \(N\) is the number of pixels for one row. * Estimated thickness error for each layer calculates absolute difference in layer thickness. The estimated thickness error for layer \(l\) can be formulated as: \[TE_{l}=\frac{\sum_{i}^{N}abs(|p^{li}|-|g^{li}|)}{N}\] where \(\left|p^{li}\right|\) and \(\left|g^{li}\right|\) denote the number of pixels belonging to layer \(l\) in column \(i\) and the ground truth one respectively. \(N\) is the number of pixels for one row. b. Quantitative evaluation  
We present quantitative evaluation of two versions of MDAN-UNet in contrast with the comparative methods in terms of the number of parameters and convolutional kernels (Table 1), mean dice score (Table 2), mean thickness error for each layer (Table 3) and mean contour error (Table 4). Figure 4: Layer segmentation comparison of a Test OCT B-scan. (**a**) A Test OCT B-scan, (**b,c**) the segmentation results completes by two different experts, (**d**) MDAN-UNet-32 prediction, (**e**) MDAN-UNet-16 prediction, (**f–i**) comparative methods’ predictions \begin{table}
\begin{tabular}{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] & 0.874 & 0.909 & 0.807 & 0.770 & 0.944 & 0.889 & 0.868 & 0.866 \\  & U-Net [10] & 0.884 & 0.917 & 0.818 & 0.793 & 0.947 & * 0.901 & 0.871 & 0.876 \\  & ReLayNet [17] & 0.884 & 0.914 & 0.811 & 0.795 & 0.945 & 0.895 & 0.870 & 0.874 \\  & CE-Net [18] & 0.885 & 0.917 & * 0.823 & 0.795 & 0.947 & 0.892 & 0.871 & 0.876 \\  & UNet++ [21] & 0.887 & 0.920 & 0.815 & 0.794 & * 0.948 & **0.902** & **0.880** & 0.878 \\  & MDAN-UNet-16 & **0.990** & * 0.923 & * 0.823 & * 0.800 & 0.950 & 0.900 & 0.874 & * 0.880 \\  & MDAN-UNet-32 & * 0.889 & **0.924** & **0.830** & **0.806** & **0.957** & **0.902** & * 0.876 & **0.883** \\ \hline \multirow{4}{*}{Expert 2} & LSE [7] & 0.868 & 0.900 & 0.802 & 0.756 & 0.944 & 0.878 & 0.845 & 0.856 \\  & U-Net [10] & 0.873 & 0.904 & 0.810 & 0.772 & 0.943 & 0.880 & 0.838 & 0.860 \\  & ReLayNet [17] & 0.870 & 0.898 & 0.805 & 0.768 & 0.945 & * 0.886 & 0.844 & 0.860 \\  & CE-Net [18] & * 0.874 & 0.903 & * 0.814 & 0.775 & **0.948** & 0.882 & 0.841 & 0.862 \\  & UNet++ [21] & 0.870 & 0.905 & 0.803 & 0.770 & * 0.947 & **0.888** & * 0.850 & 0.862 \\  & MDAN-UNet-16 & **0.877** & * 0.908 & 0.810 & * 0.777 & * 0.947 & 0.885 & 0.847 & * 0.865 \\  & MDAN-UNet-32 & * 0.874 & **0.909** & **0.818** & **0.783** & **0.948** & 0.883 & **0.848** & **0.866** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean dice score(DSC): performance comparison of layer segmentation.The best one shown in **bold** type and **’*’ marks the second best.