## 4 Experiments
### Discussion  
Our experiment demonstrates that MDAN-UNet-32 and MDAN-UNet-16 outperform the state-of-the-art methods in terms of retinal layer segmentation and fluid segmentation. We observe that the performance of fluid segmentation methods varies for OCT from different devices. There are two possible reasons for this. In the first place, each OCT volume in the RETOUCH dataset [38] corresponds to different patients, and the size and number of fluid lesions in different OCT volumes are different. For example, some OCT volumes only have a single small volume of fluid, while some OCT volumes have three kinds of fluids with large region. Therefore, the data distributions in OCT volume collected by the three devices are not consistent. Secondly, the resolutions of OCT volume from the three devices are also different. For example, an OCT volume for Circus, Spectralis and Topcon consists of 128 B-scans with a size of 512 \(\times\) 1024, 49 B-scans with a size of 512 \(\times\) 496 and 128 B-scans with a size of 512 \(\times\) 885 pixels(T-2000) or 512 \(\times\) 650(T-1000) respectively. In addition, the axial resolutions of OCT for Circus, Spectralis and Topcon are 2 \(\upmu\)m, 3.9 \(\upmu\)m and 2.6/3.5 \(\upmu\)m (T-2000/T-1000) respectively [38]. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **IRF** & **SRF** & **PED** & **ALL** \\ \hline \multirow{4}{*}{Cirrus} & U-Net [10] & 0.676(0.16) & * 0.739(0.09) & 0.485(0.21) & 0.627(0.20) \\  & UNet++ [21] & 0.646(0.23) & 0.665(0.17) & 0.500(0.14) & 0.604(0.21) \\  & MDAN-UNet-16 & * 0.724(0.11) & 0.708(0.12) & **0.530(0.19)** & * 0.662(0.17) \\  & MDAN-UNet-32 & **0.7530(0.11)** & **0.7430(0.11)** & * 0.512(0.14) & **0.6770.16)** \\ \hline \multirow{4}{*}{Spectralis} & U-Net [10] & 0.524(0.26) & 0.600(0.38) & 0.709(0.24) & 0.592(0.31) \\  & UNet++ [21] & 0.563(0.20) & **0.7450(0.27)** & * 0.741(0.24) & 0.651(0.25) \\  & MDAN-UNet-16 & **0.627(0.17)** & * 0.736(0.26) & * 0.714(0.26) & * 0.679(0.23) \\  & MDAN-UNet-32 & * 0.621(0.17) & 0.731(0.30) & **0.7540(0.22)** & **0.685(0.23)** \\ \hline \multirow{4}{*}{Topcon} & U-Net [10] & 0.652(0.14) & 0.494(0.36) & * 0.600(0.07) & 0.594(0.23) \\  & UNet++ [21] & 0.668(0.11) & 0.493(0.36) & 0.598(0.10) & 0.602(0.23) \\ \cline{1-1}  & MDAN-UNet-16 & * 0.675(0.11) & * 0.516(0.26) & 0.586(0.26) & * 0.609(0.23) \\ \cline{1-1}  & MDAN-UNet-32 & **0.706(0.10)** & **0.5300(0.38)** & **0.677(0.02)** & **0.648(0.23)** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} \end{tabular} } & average & 0.652(0.16) & 0.641(0.26) & 0.614(0.17) & 0.635(0.22) \\ \cline{1-1} \cline{2-4}  & average & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Mean dice score (standard deviation): performance comparison of fluid segmentation. The best one shown in **bold** type and ** marks the second best. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **IRF** & **SRF** & **PED** & **ALL** \\ \hline \multirow{4}{*}{Cirrus} & U-Net [10] & 0.193(0.25) & 0.132(0.12) & **0.120(0.15)** & * 0.161(0.21) \\  & UNet++ [21] & 0.203(0.26) & * 0.097(0.10) & * 0.128(0.11) & 0.164(0.20) \\  & MDAN-UNet-16 & * 0.153(0.20) & 0.098(0.10) & 0.241(0.18) & 0.171(0.19) \\  & MDAN-UNet-32 & **0.144(0.20)** & **0.085(0.07)** & 0.141(0.11) & **0.134 (0.16)** \\ \hline \multirow{4}{*}{Spectralis} & U-Net [10] & 0.080(0.09) & 0.098(0.11) & 0.096(0.06) & 0.089(0.09) \\  & UNet++ [21] & 0.104(0.12) & * 0.072(0.08) & * 0.089(0.05) & 0.092(0.09) \\  & MDAN-UNet-16 & * 0.063(0.08) & 0.091(0.10) & 0.095(0.08) & * 0.079(0.09) \\  & MDAN-UNet-32 & **0.0566(0.12)** & **0.047(0.05)** & **0.067(0.06)** & **0.056(0.09)** \\ \hline \multirow{4}{*}{Topcon} & U-Net [10] & * 0.036(0.03) & * 0.061(0.07) & 0.051(0.04) & 0.048(0.05) \\  & UNet++ [21] & * 0.036(0.03) & 0.073(0.08) & * 0.039(0.04) & 0.048(0.05) \\ \cline{1-1}  & MDAN-UNet-16 & 0.048(0.03) & **0.040(0.04)** & 0.054(0.02) & * 0.047(0.03) \\ \cline{1-1}  & MDAN-UNet-32 & **0.034(0.03)** & 0.064(0.09) & **0.026(0.004)** & **0.041(0.07)** \\ \hline \end{tabular}
\end{table}