# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Liver**} & \multicolumn{3}{c}{**Right Kidney**} & \multicolumn{3}{c}{**Left Kidney**} & \multicolumn{3}{c}{**Spleen**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-13}  & **DSC** & **mIoU** & **HD** & **DSC** & **mIoU** & **HD** & **DSC** & **mIoU** & **HD** & **DSC** & **mIoU** & **HD** & **DSC** & **mIoU** & **HD** \\ \hline \multirow{2}{*}{Segnet} & 91.5 & 85.8 & 21.6 & 87.5 & 79.7 & 17.7 & 88.7 & 80.7 & 20.6 & 84.0 & 75.1 & 20.1 & 87.9 & 80.3 & 20.0 \\  & \(\pm\) 4.5 & \(\pm\) 6.2 & \(\pm\)2.87 & \(\pm\)6.3 & \(\pm\)7.6 & \(\pm\)18.2 & \(\pm\)4.8 & \(\pm\)7.1 & \(\pm\)20.0 & \(\pm\)9.1 & \(\pm\)11.3 & \(\pm\)15.8 & \(\pm\)5.4 & \(\pm\)7.2 & \(\pm\)20.4 \\ \multirow{2}{*}{CN+VS} & 93.1 & 88.3 & 10.6 & 90.0 & 82.6 & 18.9 & 89.7 & 82.4 & 19.3 & 87.5 & 79.5 & 18.8 & 90.1 & 83.2 & 16.9 \\  & \(\pm\) 3.1 & \(\pm\) 3.4 & \(\pm\) 7.9 & \(\pm\)3.0 & \(\pm\)4.3 & \(\pm\)2.1 & \(\pm\)5.0 & \(\pm\)7.3 & \(\pm\)13.2 & \(\pm\)5.1 & \(\pm\)6.3 & \(\pm\)12.7 & \(\pm\)2.8 & \(\pm\)4.0 & \(\pm\)10.7 \\ \multirow{2}{*}{CN+VS} & 92.9 & 88.1 & 10.9 & 91.0 & 84.2 & 13.4 & 90.9 & 83.9 & 12.6 & 86.6 & 78.7 & 18.6 & 90.4 & 83.7 & 13.9 \\  & \(\pm\) 3.4 & \(\pm\) 3.9 & \(\pm\)8.1 & \(\pm\)2.2 & \(\pm\)3.2 & \(\pm\)10.1 & \(\pm\)3.1 & \(\pm\)4.8 & \(\pm\)9.6 & \(\pm\)5.8 & \(\pm\)6.7 & 13.2 & \(\pm\)3.0 & \(\pm\)3.9 & \(\pm\)8.4 \\ \hline \multirow{2}{*}{nnUNet} & 91.3 & 85.7 & 20.9 & 89.9 & 82.6 & 16.8 & 89.3 & 81.6 & 17.2 & 87.2 & 78.9 & 18.8 & 89.4 & 82.2 & 18.4 \\  & \(\pm\) 4.8 & \(\pm\)6.7 & \(\pm\)29.0 & \(\pm\)5.2 & \(\pm\)7.2 & \(\pm\)18.6 & \(\pm\)5.6 & \(\pm\)8.1 & \(\pm\)21.5 & \(\pm\)5.6 & \(\pm\)6.9 & \(\pm\)16.0 & \(\pm\)4.4 & \(\pm\)6.3 & \(\pm\)21.1 \\ \multirow{2}{*}{CN} & 92.0 & 86.7 & 20.1 & 91.6 & 84.8 & 9.1 & 91.3 & 84.3 & 13.9 & 87.5 & 79.3 & 14.2 & 90.6 & 83.8 & 14.3 \\  & \(\pm\) 4.0 & \(\pm\) 5.2 & \(\pm\)25.2 & \(\pm\)2.4 & \(\pm\)3.7 & \(\pm\)5.7 & \(\pm\)2.6 & \(\pm\)4.0 & \(\pm\)13.0 & \(\pm\)5.1 & \(\pm\)5.9 & \(\pm\)7.1 & \(\pm\)2.9 & \(\pm\)4.0 & \(\pm\)12.2 \\ \multirow{2}{*}{CN+VS} & 92.4 & 87.3 & 18.8 & 92.3 & 85.9 & 7.7 & 91.6 & 85.0 & 11.6 & 88.4 & 80.8 & 12.3 & 91.2 & 84.8 & 12.6 \\  & \(\pm\) 3.8 & \(\pm\) 4.8 & \(\pm\)2.8 & \(\pm\)2.0 & \(\pm\)3.3 & \(\pm\)5.2 & \(\pm\)2.8 & \(\pm\)4.4 & \(\pm\)12.3 & \(\pm\)4.7 & \(\pm\)5.4 & \(\pm\)6.2 & \(\pm\)2.6 & \(\pm\)3.5 & \(\pm\)10.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results of our proposed framework for the CHAOS(MRI) dataset in terms of DSC (%), mIoU (%), and HD (mm). We presented a comparison of our proposed method with related works on the CHAOS(MRI) dataset. We compared with recurrent U-Net variant BI0-Net [47] and Neural Architecture Search (NAS) networks including Auto-DeepLab [48], NAS-UNet [49], MS-NAS [50], and BiX-NAS [51]. Table 4 summarizes the comparison, providing the metrics of DSC and mIoU for the abdominal organs. Our proposed framework could achieve the best performance among all methods in DSC and mIoU. **Hippocampus:** We further evaluated our framework by segmenting the hippocampus, which has two sub-regions, anterior and posterior. Similar to the ablation study for the CHAOS(MRI) dataset, we trained the same baseline networks and implemented our framework on them. Table 5 indicates the ablation study results of our framework on DSC, mIoU,