## 3 Experiments and Results
### Experiment 2: Training Strategy Selection  
In Section 3.1, a high HD value was observed for Efficient-UNet when using 2D images. This issue can be mitigated by implementing a 2.5D strategy. The high HD values in 2D may be attributed to the consideration of individual slices, particularly affecting certain organs, like the left lung, due to their sensitivity to noise. However, 2.5D strategies, which \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **DSC** & **IoU** & **HD (mm)** & **\# Parameters** & **Training Time** & **Inference Time** \\ \hline
**Efficient-UNet** & **0.804 \(\pm\) 0.058** & **0.711 \(\pm\) 0.062** & 25.663 \(\pm\)18.724 & **20,225,550** & 4 h & **3.71 s** \\ \hline UNet & 0.761 \(\pm\) 0.078 & 0.657 \(\pm\) 0.086 & 35.915 \(\pm\) 13.632 & 30,106,806 & **3.9 h** & 7.07 s \\ \hline ResAtt UNet & 0.677 \(\pm\) 0.105 & 0.561 \(\pm\) 0.115 & **21.536 \(\pm\) 8.803** & 34,877,746 & 13.9 h & 5.37 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of UNet, Residual Attention UNet, and Efficient-UNet on the test set based on DSC, IoU, HD, number of parameters, training time (in hours), and inference time (in seconds). The best results are highlighted in bold. All metrics are computed in 3D. Figure 7: Visual comparison of the results from Efficient-UNet, Residual Attention UNet, and UNet models on the test dataset alongside the corresponding ground truth using the 2D strategy. The heart is in pink, the spinal cord is in red, the left lung is in blue, the right lung is in green, and the esophagus is in light blue. incorporate contextual information from neighboring slices, can help alleviate the impact of noise and enhance segmentation performance, ultimately resulting in improved HD values. Table 2 summarizes the metrics employed to evaluate both strategies for all organs within the test dataset, while Figure 8 presents the visual results of the 2D Efficient-UNet and the 2.5D Efficient-UNet across several patients in the same test dataset. A notable observation is that the 2.5D strategy consistently exhibited superior performance across the majority of metrics in comparison with the 2D strategies, with a notable advantage in terms of the average Hausdorff distance (HD), signifying enhanced boundary accuracy. Furthermore, the segmentation results visually reinforce this pattern, underscoring the effectiveness of the 2.5D approaches. Analyzing organ-specific performance as presented in Table 3, it is evident that the 2.5D strategy consistently yielded lower HD values for the left lung, the right lung, and the spinal cord in contrast to the 2D strategy. This suggests that the 2.5D strategy exhibits effective boundary capture with reduced deviations from the ground truth. While the 2D strategy achieved higher IoU and DSC scores for the right lung and the heart compared to the 2.5D strategy, it still resulted in potential boundary inaccuracies. In terms of anatomical metrics, specifically the volume measurements, the 2.5D strategy demonstrated an overall higher correlation when compared to the 2D strategy, albeit with a minimal difference. However, when considering the Bland-Altman analysis, the 2D strategy appeared to be more favorable. Nevertheless, upon examining the organ-specific values, it is evident that the volume metrics closely align across the majority of organs, exhibiting minimal variation. The sole exception is observed in the case of the spinal cord, where all the metrics suggest that the 2.5D strategy outperformed the 2D strategy. Overall, there is some indication that the 2.5D strategy may exhibit improved organ segmentation performance, particularly in boundary delineation, when compared to the 2D strategy, though we should acknowledge the possibility of certain organ-specific variations in the performance metrics.