## 4 Experiments
### Result
#### 4.3.1 Experimental Results of Multi-Scale Organ Information Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**Average**} & \multirow{2}{*}{**Aorta \(\uparrow\)**} & \multirow{2}{*}{**GB \(\uparrow\)**} & \multirow{2}{*}{**KL \(\uparrow\)**} & \multirow{2}{*}{**KR \(\uparrow\)**} & \multirow{2}{*}{**Liver \(\uparrow\)**} & \multirow{2}{*}{**PC \(\uparrow\)**} & \multirow{2}{*}{**SP \(\uparrow\)**} & \multirow{2}{*}{**SM \(\uparrow\)**} \\ \cline{3-3} \cline{8-11}  & & & & & & & & & & & \\ \hline V-Net [54] (2016) & CNN & 68.81 & - & 75.34 & 51.87 & 77.1 & 80.75 & 87.84 & 40.05 & 80.56 & 56.98 \\ \hline DARR [51] (2020) & CNN & 69.77 & - & 74.74 & 53.77 & 72.31 & 73.24 & 94.08 & 54.18 & 89.9 & 45.96 \\ \hline UNet [4] (2015) & CNN & 70.11 & 44.69 & 84 & 56.7 & 72.41 & 62.64 & 86.98 & 48.73 & 81.48 & 67.96 \\ \hline AttnUNet [55] (2018) & CNN & 71.7 & 34.47 & 82.61 & 61.94 & 76.07 & 70.42 & 87.54 & 46.7 & 80.67 & 67.66 \\ \hline R50+UNet [7] (2021) & CNN & 74.68 & 36.87 & 84.18 & 62.84 & 79.19 & 71.29 & 93.35 & 48.23 & 84.41 & 73.92 \\ \hline R50+AttnUNet [7] (2021) & CNN & 75.57 & 36.97 & 55.92 & 63.91 & 79.2 & 72.71 & 93.56 & 49.37 & 87.19 & 74.95 \\ \hline TransUNet [7] (2021) & CNN+Transformer & 77.48 & 31.69 & 87.23 & 63.13 & 81.87 & 77.02 & 94.08 & 55.86 & 85.08 & 75.62 \\ \hline SSFFormerPVT [56] (2022b) & CNN+Transformer & 78.01 & 25.72 & 82.78 & 63.74 & 80.72 & 78.11 & 93.53 & 61.53 & 87.07 & 76.61 \\ \hline PolypPVT [57] (2021) & CNN+Transformer & 78.08 & 25.61 & 82.34 & 66.14 & 81.21 & 73.78 & 94.37 & 59.34 & 88.05 & 79.4 \\ \hline MT-UNet [58] (2022a) & CNN+Transformer & 78.59 & 26.59 & 87.92 & 64.99 & 81.47 & 77.29 & 93.06 & 59.46 & 87.75 & 76.81 \\ \hline Swin-Unet [35] (2021) & Transformer & 79.13 & 21.55 & 85.47 & 66.53 & 83.28 & 79.61 & 94.29 & 56.58 & 90.66 & 76.6 \\ \hline PVT-CASCADE [52] (2023) & CNN+Transformer & 81.06 & 20.23 & 83.01 & **70.59** & 82.23 & 80.37 & 94.08 & 64.43 & 90.1 & 83.69 \\ \hline Missformer [31] (2021) & Transformer & 81.96 & 18.2 & 86.99 & 68.65 & 85.21 & 82 & 94.41 & 65.67 & **91.92** & 80.81 \\ \hline CASTformer [59] (2022) & CNN+Transformer & 82.55 & 22.73 & **89.05** & 67.48 & 86.05 & 82.17 & **95.61** & **67.49** & **91** & 81.55 \\ \hline TransCASCADE [52] (2023) & CNN+Transformer & 82.68 & **17.34** & 86.63 & 68.48 & **87.66** & **84.56** & 94.43 & 65.33 & 90.79 & 83.52 \\ \hline MINTFormer (ours) & CNN+Transformer & **84.74** & **14.03** & 87.62 & **73.95** & **89.27** & **86.47** & **95.38** & **69.72** & 90.66 & **84.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synapse experiment results, best result is highlight in red, and the second best result is highlight in blue, \(\uparrow\) indicates higher values are better, \(\downarrow\) indicates lower values are better. aorta. However, MINTFormer is the only model that accurately predicts both organs, substantiating its exceptional capacity to discern intricate details and distinguish between relevant and irrelevant structures. In Figure 10b the area encircled in yellow represents the left kidney. It is evident that MINTFormer has performed the most comprehensive segmentation of the left kidney. Missformer, TransUnet, and SwinUnet are unable to segment the relatively complete left kidney due to their inability to maintain multi-scale information. In Figure 10c the area highlighted in yellow represents the same left kidney, but it has been relocated to a different channel dimension, and the ground truth (GT) value of the left kidney has decreased accordingly. Missformer preserves some of the features of the left kidney due to the fixed-size convolution operation. However, SwinUnet does not employ a convolution operation to maintain sub-features during upsampling, resulting in its inability to detect the left kidney. MINTFormer successfully captures the multi-scale spatial information of the left kidney due to the utilization of a multi-Scale Sampling Decoder. This enables MINTFormer to correctly identify the left kidney across different channel dimensions.