## 5 Discussion  
Although the automatic segmentation of brain tumors, particularly gliomas, from MRI scans has been widely investigated in the literature, most studies have not addressed the need for fine-grained segmentation of key tumor subregions such as necrotic areas and peritumoral edemas. Nonetheless, these regions are associated with distinct pathological features that can guide the diagnosis and prognosis of adult-type diffuse gliomas [13]. Our segmentation results demonstrate that the proposed multi-encoder design with the ability to process MRI modality-specific information and apply channel-wise recalibration performs particularly well on tumor regions with more distinct imaging characteristics while still handling more complex subregions such as necrosis with outstanding accuracy. Our comparison with the benchmark SegFormer3D transformer-based model for medical image segmentation showed that this model struggles to capture finer details, while our  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Subregion** & \multicolumn{3}{c}{_SegFormer3D_} & \multicolumn{3}{c}{_Multi-Encoder_} \\ \cline{2-7}  & **Dice** & **Precision** & **Recall** & **Dice** & **Precision** & **Recall** \\ \hline NCR \({}^{*}\) & 0.66 \(\pm\) 0.29 & 0.68 \(\pm\) 0.28 & 0.74 \(\pm\) 0.26 & **0.78 \(\pm\) 0.28** & **0.84 \(\pm\) 0.24** & **0.79 \(\pm  
attention-enhanced multi-encoder design is particularly effective in segmenting challenging and small-scale tumor components such as NCR and ED. Figure 4 shows four representative cases sampled from the test set, illustrating a qualitative comparison between the segmentation results produced by Segformer3D and our multi-encoder model along with the corresponding ground-truth masks. Qualitative results confirm that both models are overall capable of identifying tumor subregions, although with visible differences in their precision and consistency. For instance, the lower precision for ED and ET achieved by SegFormer3D and highlighted in quantitative analysis is reflected by an overestimation of these regions. This can be explained by considering that despite its transformer-based hierarchical structure and lightweight design, Segformer3D may not fare well when segmenting fine-grained details due to its reduced preservation of local continuity. In contrast, our architecture features separate encoding paths for each MRI modality, which allows for more effective usage of complementary information. This modular design enables the network to learn modality-specific representations prior to feature fusion, improving the accuracy of subregion-level predictions. Additionally, the integration of Squeeze-and-Excitation blocks enhances the model's ability to focus on salient features, particularly in smaller or less clearly-defined regions such as necrotic tissue. The presence of modular attention blocks within each encoder further promotes focused refinement of features prior to integration, leading to more discriminative fused representations. Overall, the statistically significant improvements across all performance metrics and regions (\(p<0.001\)) suggest that our customized architecture leveraging both CNN- and attention-based characteristics outperforms state-of-the-art segmentation models while also maintaining a reduced number of 11.2 M parameters. Computational profiles of all the tested models are summarized in Table 3.  
Figure 4: Visual comparison of segmentation results for some representative cases sampled from the test set. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Mean Dice ET** & **Mean Dice NCR** & **Mean Dice ED** & **Inference Time (s)** & **Peak Memory (MB)** & **Parameters (M)** \\ \hline SegFormer3D & 0.79 & 0.66 & 0.78 & 0.65 & 1939.5 & \(\sim\)4.5 \\ Multi-encoder & 0.88 & 0.78 & 0.86 & 1.41 & 5487.6 & \(\sim\)11.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of models in terms of performance and computational requirements.