# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 4 Experiments and Results
### Comparison with Other Methods  
To evaluate the advantages of the proposed model, we compared twelve advanced models. The numbers of networks compared were 2, 4, 2, 1, 1, and 2 for 2024, 2023, 2022, 2020, 2019, and 2016, respectively. 3D UNet, UNet++, V-Net, DMFNet, MBANet, HMNet, GMetaNet, RFTNet, SPA-Net and ETUNet are architecture variants based on basic UNet, and TransUNet and mmformer are structures based on Transformer. Table 2 and Figures 5 and 6 show that the Dice coefficients of SARFNet are 90.40, 85.54, and 82.15, and the HDs are 5.95, 7.30, and 2.72 for the three tumor subregions (WT, TC, and ET), respectively. We compared the traditional 3D UNet as the baseline, and our WT, TC, and ET results increased by 1.87, 13.77, and 6.19, respectively. In the experiment, we conducted multiple comparisons, including the variants based on UNet that perform brain tumor image segmentation tasks well, as well as CNN + Transformer architecture based on Transformer. The implementation foundation of Transformer is self-attention, which is an attention mechanism that can establish global dependencies and expand the receptive field of images. Compared to CNN, it has a larger receptive field and can capture more contextual information. It achieves better performance by selecting important information and ignoring unimportant information. However, this results in its ability to capture effective local information to be lower than CNN's. Recently, many networks based on Transformer have also incorporated CNN for local characteristics. Although there has been a significant improvement in the results of brain tumor segmentation tasks, it has not achieved the best performance of brain tumor segmentation tasks, due to only adding local features without considering maintaining the receptive field. As a result, some networks based on Transformer have poorer results than the networks based on UNet. The attention mechanism is currently working well in networks based on UNet architecture. However, due to its inferior ability to establish accurate global relationships compared to Transformer, the loss of global relationship information affects the final segmentation performance. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Dice Score (\%)**} & \multicolumn{4}{c}{**Hausdorff Distance (mm)**} \\ \cline{2-9}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline
3D U-Net [40] (2016) & 88.53 & 71.77 & 75.96 & 78.75 & 17.10 & 11.62 & 6.04 & 11.59 \\ UNet++ [41] (2020) & 88.76 & 82.05 & 77.94 & 82.92 & 6.31 & 8.64 & 4.82 & 6.59 \\ V-Net [42] (2016) & 89.60 & 81.00 & 76.60 & 82.40 & 6.54 & 7.82 & 7.21 & 7.19 \\ DMFNet [43] (2019) & 89.90 & 83.50 & 78.10 & 83.83 & 4.86 & 7.74 & 3.38 & 5.33 \\ TransUNet [54] (2022) & 89.95 & 82.04 & 78.38 & 83.46 & 7.11 & 7.67 & 4.28 & 6.35 \\ mmformer [55] (2022) & 89.56 & 83.33 & 78.75 & 83.88 & **4.43** & 8.04 & 3.27 & 5.25 \\ GMetaNet [56] (2023) & 90.10 & 84.20 & 82.00 & 85.40 & 5.16 & **5.26** & 2.62 & 4.34 \\ MBANet [44] (2023) & 89.80 & 85.47 & 80.18 & 85.15 & 5.13 & 5.65 & **2.47** & 4.42 \\ HMNet [45] (2023) & 90.10 & 84.30 & 78.60 & 84.33 & 4.73 & 7.73 & 2.70 & 5.05 \\ FFTNet [67] (2023) & 90.30 & 82.15 & 80.24 & 84.23 & 5.97 & 6.41 & 3.16 & 5.18 \\ SPA-Net [68] (2024) & 89.63 & **85.89** & 79.90 & 85.14 & 4.79 & 5.40 & 2.77 & **4.32** \\ ETUNet [57] (2024) & 90.00 & 85.20 & 81.00 & 85.40 & 6.67 & 7.40 & 6.01 & 6.69 \\
**SARFNet (Ours)** & **90.40** & 85.54 & **82.15** & **86.03** & 5.95 & 7.30 & 2.72 & 5.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods on BraTS2018 validation dataset. In brain tumor segmentation tasks, both global relationships and local detail features are crucial. The purpose of the network we propose is to maintain global relationships as much as possible, thereby improving performance in brain tumor segmentation.