## 3 Results  
To demonstrate the efficacy of the residual attention network, we compared the result of the modified ResAttU-Net against the plain U-Net and Attention U-Net architectures. These networks were trained on our MRI dataset. The mean slice number per sample was 77 slices containing the prostate and the OARs (femoral heads, bladder, and rectum). All the architectures were evaluated using k-fold cross-validation, with approximately 8.5 h of training per fold. Firstly, the U-Net, Attention U-Net and ResAttU-Net were trained with the 2D training strategy. Regarding the evaluation metrics, ResAttU-Net performed slightly better than the other architectures (Table 1). Concerning the DSC, the differences were significant between U-Net and ResAttU-Net (\(p=0.01\)) but not between Attention U-Net and ResAttU-Net (\(p=0.10\)). On the other hand, for the HD, the differences were always not significant (\(p=0.11\) between U-Net and ResAttU-Net and \(p=0.49\) between Attention U-Net and ResAttU-Net). Even if the differences were not always significant, we decided to keep the model that provided the best results.  
The second task was the evaluation of the ResAttU-Net in the 2.5D strategy. Table 2 shows the results for each organ and the overall performance between the 2D and the 2.5D training strategies. It is worth noting that the bladder showed the best performance, yielding 0.92 DSC. The prostate underperformed the other organs with the lowest DSC value of 0.80. The presence of seminal vesicles is the cause of the low DSC on prostate segmentation. Moreover, the comparison between the different training strategies shows improvements with the 2.5D strategy in all the organs apart from the rectum and the femoral head (right). However, the results are close between the two strategies for these organs. Qualitative results are shown in Figures 7 and 8. Considering the performance, the 2.5D strategy is 0.001 and 0.03 seconds (s) per slice slower than 2D using GPU and CPU, respectively (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architectures** & **Dice \(\pm\) STD** & **HD \(\pm\) STD (mm)** \\ \hline U-Net & \(0.83\pm 0.14\) & \(7.95\pm 6.03\) \\ Attention U-Net & \(0.84\pm 0.12\) & \(7.50\pm 6.18\) \\ ResAttU-Net & \(\textbf{0.85\pm 0.11}\) & \(\textbf{7.49\pm 6.54}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of 3 different architectures in 2D strategy. Best results in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **2D** & \multicolumn{2}{c}{**2.5D**} \\
**Organs** & **Dice \(\pm\) STD** & \begin{tabular}{c} **HD \(\pm\) STD** \\ **(mm)** \\ \end{tabular} & **Dice \(\pm\) STD** & \begin{tabular}{c} **HD \(\pm\) STD** \\ **(mm)** \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} Rectum \\ Bladder \\ Femoral head right \\ Femoral head left \\ Femoral head left \\ Prostate \\ \end{tabular} &
\begin{tabular}{c} 0.84 \(\pm\) 0.12 \\ 0.89 \(\pm\) 0.12 \\ 0.87 \(\pm\) 0.08 \\ 0.87 \(\pm\) 0.08 \\ 0.88 \(\pm\) 0.08 \\ 0.  
Figure 8: Segmentation using 2.5D ResAttU-Net. The manual delineation is highlighted with red and the predicted of our model with green.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategies** & **CPU (s)** & **GPU (s)** \\ \hline
2D & 1.45 & 0.113 \\
2.5D & 1.48 & 0.114 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inference time estimation with 2D and 2.5D training strategies. The times shown are seconds per slice.  
Figure 7: Sample visualization in 3D of a predicted volume.