## 4 Experiments and Results
### Methods Analysis on the LiTS Dataset  
To further validate our approach, we also train and test on the LiTS dataset and compare our method with some networks, that is, UNet [16], [PERSON]'s network [9], and [PERSON]'s network [17], as shown in Table 5. Since these networks all use 2D networks and ignore 3D spatial information, the segmentation accuracy is generally not high. The dice scores of the liver segmentation in our method achieve 96.1% and the result far exceed those 2D methods. The 3D VNet is essentially a 3D UNet-like model. Compared with 3D VNet, the proposed method is 2.2% higher in Dice Global. [PERSON] et al. [54] and H-DenseUNet [18] have both used 3D network structures. Their methods have a deeper level of networking with 3D networking structures. Therefore, both Dice Per Case and Dice Global in their methods are higher than the 2D network methods. However, the large number of parameters and the utilization of computing resources are fatal disadvantages  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **VOE** & **RVD** & **ASD** & **RSMS** & **Dice** \\ \hline
2D UNet [16] & 62.5 \(\pm\) 22.3 & 0.38 \(\pm\) 1.95 & 11.1 \(\pm\) 12.0 & 16.7 \(\pm\) 13.8 & 0.51 \(\pm\) 0.25 \\
2D FCN [9] & - & - & - & - & 0.56 \(\pm\) 0.26 \\ [PERSON] et al. [14] & 56.4 \(\pm\) 13.6 & \(-\)0.41 \(\pm\) 0.21 & 6.3 \(\pm\) 3.7 & **11.6 \(\pm\) 7.6** & 0.60 \(\pm\) 0.12 \\ [PERSON] et al. [18] & **49.7 \(\pm\) 5.2** & \(-\)0.33 \(\pm\) 0.10 & 5.29 \(\pm\) 6.1 & 11.1 \(\pm\) 29.1 & 0.65 \(\pm\) 0.02 \\ our method & 57.5 \(\pm\) 13.8 & \(-\)**0.18 \(\pm\) 0.12** & **4.8 \(\pm\) 3.7** & 15.9 \(\pm\) 12.3 & **0.68 \(\pm\) 0.15** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of tumor segmentation results on the 3 DIRCADb dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{2}{c}{**Tumor**} & \multicolumn{2}{c}{**Liver**} \\ \cline{2-4}  & **Dice Per Case** & **Dice Global** & **Dice Per Case** & **Dice Global** \\ \hline Cross-entropy loss & 62.4 & 67.7 & 91.2 & 92.6 \\ Dice loss & 65.7 & 68.3 & 92.3 & 93.4 \\ Combined loss & 68.2 & 71.2 & 92.6 & 93.8 \\ Contour loss & 73.8 & 75.6 & 94.1 & 95.2 \\ Boundary loss & **74.5** & **77.2** & **94.3** & **96.1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different loss functions on the LiTS dataset (Dice: %).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **VOE** & **RVD** & **ASD** & **RSMS** & **Dice** \\ \hline
2D UNet [16] & 14.2 \(\pm\) 5.7 & \(-\)0.05 \(\pm\) 0.1 & 4.3 \(\pm\) 3.3 & 8.3 \(\pm\) 7.5 & 0.923 \(\pm\) 0.03 \\
2D FCN [9] & 10.7 & \(-\)1.4 & 1.5 & 24.0 & 0.943 \\ [PERSON] et al. [14] & 11.6 \(\pm\) 4.1 & \(-\)0.03 \(\pm\) 0.06 & 3.9 \(\pm\) 3.9 & 8.1 \(\pm\) 9.6 & 0.938 \(\pm\) 0.02 \\ [PERSON] et al. [53] & **9.2** & \(-\)11.2 & **1.6** & 28.2 - & \\ [PERSON] et al. [52] & - & - & - & - & 0.945 \\ our method & 8.5 \(\pm\) 6.6 & **0.01 \(\pm\) 0.02** & 1.6 \(\pm\) 2.0 & **3.9 \(\pm\) 6.5** & **0.961 \(\pm\) 0.08** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of liver segmentation results on the 3 DIRCADb dataset.  
in these methods. As shown in Table 5, compared with H-DenseUNet [18], our method obtains a performance improvement of 2% of Dice Per Case for the tumor segmentation. Although our liver segmentation score is not higher than those from H-DenseUNet and [PERSON]'s method, both are 3D networks, our method requires fewer resources. H-DenseNet with a batch size of 1, the resolution of the input image is \(5\times 320\times 320\) with our method. The H-DenseNet inputs the image with a resolution of \(12\times 224\times 224\), that is, 24 GB VRAM, but our method only uses 4 GB VRAM.