## 4 Discussion  
In our study on multiorgan segmentation using MR-Linac-acquired images in the thoracic region, we focused on critical organs, like the heart, the right lung, the left lung, the spinal cord, and the esophagus, driven by clinical requirements. Our dataset, consisting of 81 MRI studies and radiation therapy structure sets, formed a solid foundation for training our Efficient-UNet model. We eliminated extraneous anatomical structures, applied image enhancement techniques, and utilized an EfficientNet B4 encoder for our automatic segmentation task. The complexity in the anatomy of the organs reflects different  
Figure 8: Comparison of training strategies using the Efficient UNet architecture for different patients in the test set. The first column displays the ground truth image, the second column shows the 2D strategy, and the third column presents the 2.5D strategy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Organ**} & \multirow{2}{*}{**Strategy**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & & & \multicolumn{2}{c}{**Volume**} \\ \cline{3-6}  & & **IoU** & **DSC** & **HD (mm)** & \multicolumn{2}{c}{**Band-Altman**} & **Correlation** \\ \hline \multirow{2}{*}{Heart} & 2D & **0.860 \(\pm\) 0.018** & **0.924 \(\pm\) 0.010** & 19.403 \(\pm\) 19.860 & **235.939 \(\pm\) 108.410** & **0.983 \(\pm\) 0.009** \\ \cline{2-6}  & **2.5D** & 0.856 \(\pm\) 0.037 & 0.922 \(\pm\) 0.021 & **11.270 \(\pm\) 8.454** & 237.638 \(\pm\) 84.649 & 0.975 \(\pm\) 0.018 \\ \hline \multirow{2}{*}{Esophagus} & 2D & 0.356 \(\pm\) 0.116 & 0.513 \(\pm\) 0.134 & **10.772 \(\pm\) 5.280** & 74.510 \(\pm\) 16.860 & **0.454 \(\pm\) 0.205** \\ \cline{2-6}  & **2.5D** & **0.384 \(\pm\) 0.078** & **0.551 \(\pm\) 0.080** & 11.710 \(\pm\) 5.689 & **66.017 \(\pm\) 13.650** & 0.422 \(\pm\) 0.254 \\ \hline \multirow{2}{*}{Spinal cord} & 2D & 0.534 \(\pm\) 0.133 & 0.685 \(\pm\) 0.121 & 13.490 \(\pm\) 11.250 & 13.835 \(\pm\) 4.430 & 0.223 \(\pm\) 0.138 \\ \cline{2-6}  & **2.5D** & **0.585 \(\pm\) 0.096** & **0.733 \(\pm\) 0.077** & **9.600 \(\pm\) 5.920** & **12.770 \(\pm\) 5.130** & **0.304 \(\pm\) 0.164** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._performances of our method. Firstly very good segmentation accuracy is observed for certain organs, especially for the lung and the heart, as demonstrated by the evaluation metrics. On the other hand, more questionable results are obtained for the spinal cord and the esophagus. A first reason could relate to the size of the organs. The IoU and the DSC are sensitive to the number of pixels and a small error in small anatomic structure segmentation can have a big impact on these metrics. In particular, the spinal cord is defined with a few pixels in each image plane. Concerning the esophagus, the manual segmentation of this organ could be difficult due to its complexity and may generate high inter-observer variation. Imprecise manual segmentation could reesult in compromised training of our network. Future work will seek to include a consensus of experts for the segmentation of this organ in order to create a robust ground truth. Our 2.5D training strategy, combining 2D and 3D methods, improved the boundary segmentation accuracy and the overall segmentation performance. Overall, the EfficientUNet model and the 2.5D strategy appear to be the better option (Figure 9), even if segmentation with a 2D strategy sometimes provides better metrics results (such as the DSC for the lung). However, in cases where there is a discrepancy between the DSC and the HD, the better results for the DSC with a 2D strategy is compensated by an important decrease in the HD with a 2.5 strategy (as for the segmentation of the right lung). Sometimes a difference between the results obtained with the 2D and 2.5D strategies is not clearly evident. However, the benefit of the 2.5D approach may be more apparent in challenging cases, such as those with complex anatomical variations or pathologies.