## 4 Experiments and Results
### Comparison of Models  
We compared our proposed G-UNETR++ with various state-of-the-art methods to evaluate its effectiveness, robustness, and generalization on the LiTS, Sliver07, and 3D-IRCADb datasets. Table 6 shows the comparison results of the proposed method with nine state-of-the-art methods on the LiTS dataset. The proposed method outperformed all the other methods in terms of DSC, VOE, ASSD, and MSSD. The proposed method achieved a maximum improvement of 3% in the main DSC evaluation metric. For VOE, ASSD, and MSSD, the proposed method outperformed the other methods by 0.39-4.91%, 0.38-1.66 mm, and 3.11-27.40 mm, respectively. Table 7 shows the comparison results of the proposed method with three state-of-the-art methods on the Sliver07 dataset. The proposed method achieved the best performance in terms of DSC, VOE, ASSD, and MSSD. For DSC, VOE, ASSD, and MSSD, the proposed method outperformed the other methods by 0.47-2.32%, 0.98-4.12%, 0.42-2.09 mm, and 5.46-40.81 mm, respectively. Table 8 shows the comparison results of the proposed method with eight state-of-the-art methods on the 3D-IRCADb dataset. The proposed method achieved the best in terms of DSC, VOE, and ASSD, and the second best in terms of MSSD. For DSC, VOE, and ASSD, the proposed method outperformed the other methods by 0.09-2.30%, 0.29-4.85%, and 0.17-2.62 mm, respectively. The results showed that the proposed method achieved excellent segmentation performance, even on both non-training datasets (Sliver07 and 3D-IRCADb datasets). Therefore, the proposed method showed strong effectiveness, robustness, and generalizability for accurate liver segmentation using CT scans. Figure 8 shows segmentation results of the proposed method over the three different datasets with one example from each dataset. As shown in Figure 8, the green region represents the ground truth, and the red line represents the liver boundary from our segmentation results. Even for the challenging cases with tumors, our segmentation results were perfectly aligned with the ground truth.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **DSC** & **VOE** & **RAVD** & **ASSD** & **MSSD** \\ \hline FCN + ACM [35] & 0.9430 & - & - & 2.30 & 34.70 \\ MA-Net [47] & 0.9597 & 0.1000 & 0.0600 & - & - \\ S-Net + T-Net + SEL & 0.9680 & 0.0700 & 0.0150 & - & - \\ + PRL [36] & 0.9680 & 0.0688 & 0.0146 & 1.37 & 37.60 \\ DefED-Net [37] & 0.9650 & 0.0670 & 0.0090 & 1.22 & - \\ HRU-Net [39] & 0.9660 & 0.0550 & 0.0050 & 1.02 & 40.37 \\ DHT-Net [40] & 0.9660 & 0.0670 & 0.0090 & 1.12 & - \\ CotePRes-Net [41] & 0.9688 & 0.0578 & 0.0039 & 1.09 & 16.08 \\ DRAUNet [8] & 0.9710 & 0.0548 & 0.0108 & 1.31 & - \\ Proposed & 0.9738 & 0.0509 & 0.0201 & 0.64 & 12.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different models on LiTS dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **DSC** & **VOE** & **RAVD** & **ASSD** & **MSSD** \\ \hline Without & 0.9737 & 0.0511 & 0.0201 & 0.6510 & 18.4870 \\ post-processing & & & & & \\ With & 0.9738 & 0.0509 & 0.0201 & 0.6411 & 12.9686 \\ post-processing & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study for post-processing over the proposed G-UNETR++ on LiTS dataset.