# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 5 Discussion and Conclusions  
An appropriately scaled receptive field is crucial for brain tumor segmentation tasks, and making good use of the receptive field can effectively enhance the performance of brain tumor segmentation. In this paper, we proposed a SARFNet architecture based on UNet that integrates the SL\({}_{4}\)RF module, AAM module, CAM, and deep supervision to improve brain tumor segmentation performance. The SL\({}_{4}\)RF module is developed for downsampling to reduce the image size and expand the receptive field to extract multi-scale information. The AAM module in the bottleneck layer can capture global information while preserving the advantages of convolution. The CAM is used in the skip connections of the third and fourth layers, using high-level semantic information to guide low-level semantic information to help restore images during the upsampling stage. In addition, we compared it with networks based on the UNet and Transformer from 2022 to 2024. Our results show Dice values of 90.40, 85.54, and 82.15 and HD values of 5.95, 7.30, and 2.72, respectively, for the three tumor subregions (WT, TC, and ET). The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks. This indicates that the model and modules we proposed can achieve accurate brain tumor segmentation and enhance the performance of the segmentation. By comparing with a traditional 3D UNet, we found that there were significant improvements in the segmentation of small targets. Compared with variant networks based on UNet and Transformer, our achievement lies in the fact that our proposed network \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c}{**Dice Score (\%)**} \\ \cline{2-5}  & **WT** & **TC** & **ET** & **AVG** \\ \hline
**A** & **90.61** & 85.25 & 79.87 & 85.25 \\
**B** & 90.20 & 84.95 & 78.39 & 84.52 \\
**C (SARFNet)** & 90.40 & **85.54** & **82.15** & **86.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dice results of ablation experiments for axial dimension of AAM module. Figure 8: Dice result of ablation experiments for axial dimension of AAM module. uses different convolution kernels according to the characteristics of each layer in the network, effectively maintains the relationship between the receptive field and global features, and prevents the loss of important information during the feature processing. Specifically, we also conducted ablation experiments on the SL\({}_{\text{t}}\)RF module, AAM module, CAM, and deep supervision, which demonstrated the effectiveness of our modules. From Table 4, it is known that, in our network, features become more detailed as the layers deepen. In this case, the information extracted by larger convolutional kernels is sufficient, and there is also an improvement in performance. This indicates that, in the deeper layers of the network, the size of the receptive field has an impact on the effective extraction of features. The results in Table 5 show that, in the network we proposed, the deeper the network layers, the more necessary it is to use high-level semantic information to guide the selection of lower-level semantic information. However, our research also had some limitations. The extensive use of attention mechanisms will inevitably increase the complexity of the model. Therefore, we aim to perform lightweight research on models in the future. Although our network exhibited better performance when tested on the brain tumor dataset for tumor segmentation tasks, we have not attempted to evaluate images unrelated to brain tumors, such as RGB images. In the next stage, we aim to increase the scope of our experiments to make the network we proposed more practical. We believe that the encouraging results obtained with SARFNet will inspire further research into brain tumor segmentation. Conceptualization, N.C. and B.G. ; Methodology, B.G. ; Software, P.Y. ; Data Curation, R.Z. ; Writing--Original Draft, B.G. ; Writing--Review and Editing, N.C. and B.G.