## 4 Experiments
### Quantitative Results  
This section compares the proposed network with five state-of-the-art methods to evaluate its effectiveness and robustness on the CE-MRI dataset. 3 DUNet [60] is widely used for medical image segmentation. It includes an encoder for downsampling to extract features, a decoder for upsampling to generate the segmentation map and skip connections that bridge the encoder and decoder to recover information lost during the downsampling phase. The structure of V-Net [61] is like 3 DUNet but employs residual connections to enhance feature learning across layers. nnU-Net [62] presents a framework that automatically adapts to different datasets, built upon the foundation of standard U-Nets. UNETR [43] is a Transformer-based medical image segmentation network that combines the advantages of U-Net and Transformer, uses the self-attention mechanism to capture the global information in the image, and uses U-Net's encoder-decoder structure to perform local feature extraction and upsampling. SwinUNetR [46] uses Swin-Transformer for feature extraction and encoding. Swin-Transformer is a new type of Transformer that uses a layered local attention mechanism to capture global information in an image while using a cross-layer attention mechanism to promote feature transmission and integration. Comparing the proposed method with other advanced methods, DSC, 95 HD and ASD are used to measure the segmentation performance of the model for liver and tumor, respectively. As shown in Table 1, the proposed method outperforms other approaches across five metrics, achieving a DSC score of \(0.9662\pm 0.0219\) for liver segmentation and \(0.8647\pm 0.0256\) for tumor segmentation. The proposed model outperforms other segmentation networks on tumor segmentation by a large margin and slightly outperforms other models on liver segmentation. This is mainly because the proposed model can segment the small tumor more accurately. Table 2 compares the computational cost and parameter size of our method with several representative 3D medical image segmentation networks. Although transformer-based architectures are generally associated with increased computational burden, our approach demonstrates a favorable trade-off between accuracy and efficiency. Notably, while our model achieves competitive performance (as shown in Table 1), it only introduces 63.4 M parameters and 188.6 GFLOPs, which is comparable to SwinUNetR (62.1 M, 237.9 GFLOPs) and significantly lower than nnU-Net (91.2 M, 213.8 GFLOPs). Compared with UNetR and SwinUNetR, our method shows only a modest increase in parameters (by 4.9 M and 1.3 M, respectively) and even a notable reduction in GFLOPs compared to SwinUNetR. These results suggest that our design is efficient in terms of computational complexity and model size and that the performance gains do not come at the expense of a significant increase in resource consumption. To evaluate the statistical significance of the improvements made by our model, A paired \(t\)-test was conducted between the others and our model, with the calculation of the two-sided \(p\)-value. A threshold of 0.05 was set for significance. In Table 3, we report the p-values for liver and tumor based on DSC. The results indicate a statistically significant difference between all the compared models and our model, with the lowest different \(p\)-values observed in comparison to nnU-Net: 0.0151 for liver and 0.0032 for tumor. To determine if the contours predicted by our model are generally larger or smaller than the annotated ones, we calculated the Volume Similarity for all models, and the results are shown in Table 4. It was observed that the majority of models tend to predict smaller