## 3 Results
### PVS Segmentation Results  
Table 4 and Figure 5 display the PVS segmentation performance of the nnU-Net and SynthSeg models on the internal and external test sets in terms of DSC, HD95, and surface DSC. In terms of the differences between the PVS segmentations resulting from the automated ventricle segmentations, the nnU-Net had a significantly higher DSC and surface DSC in the internal test set compared to the external set (DSC = 0.87 [0.82-0.89] vs. 0.76 [0.69-0.79], \(p\) < 0.001; Surface DSC = 0.85 [0.76-0.91] vs. 0.80 [0.69-0.83], \(p\) < 0.001). In addition, the volume of the segmentation was significantly higher in the internal dataset (86.8 cm\({}^{3}\) [63.6-110.0] vs. 59.7 cm\({}^{3}\) [52.3-75.1], \(p\) < 0.001).  
\begin{table}
\begin{tabular}{l l l} \hline \hline Segmentations & \multicolumn{2}{c}{Clinical Rating, Mean \(\pm\) SD} \\ \hline \hline \multicolumn{2}{l}{Internal dataset} \\ \hline - & GT & 3.5 \(\pm\) 0.8 \\ \hline - & nnU-Net & 3.8 \(\pm\) 0.5 \\ \hline - & SynthSeg & 2.6 \(\pm\) 0.5 \\ \hline \hline \multicolumn{2}{l}{External dataset} \\ \hline - & GT & 2.9 \(\pm\) 0.3 \\ \hline - & nnU-Net & 3.6 \(\pm\) 0.6 \\ \hline - & SynthSeg & 2.6 \(\pm\) 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the clinical rating of the ventricle segmentations. Segmentations were scored on a 4-point scale: (1) Not usable; (2) Major adjustments needed; (3) Acceptable/minor adjustments; (4) Good.  
The SynthSeg model had a significantly higher DSC in the internal test set compared to the external test (DSC = (0.80 [0.72-0.83] vs. 0.74 [0.64-0.78], \(p\) < 0.001). In addition, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & MODEL & DSC [-] & HD95 [mm] & Surface DSC & Volume & Volume \\  & & & & [-] & GT [cm\({}^{3}\)] & Segmentation [cm\({}^{3}\)] \\ \cline{2-7}  & nnU-Net & 0.87 (0.82–0.89) & 3.1 (1.8–4.6) & 0.85 (0.76–0.91) & 77.2 (57.2–96.7) & 86.8 (63.6–110.0) \\ \cline{2-7}  & SynthSeg & 0.80 (0.72–0.83) & 3.1 (2.4–13.3) & 0.73 (0.29–0.77) & 77.2 (57.2–96.7) & 85.5 (60.1 –106.7) \\ \hline  & nnU-Net & 0.76 (0.69–0.79) & 2.3 (2.0–6.2) & 0.80 (0.69–0.83) & 78.3 (68.6–92.1) & 59.7 (52.3–75.1) \\ \cline{2-7}  & SynthSeg & 0.74 (0.64–0.78) & 2.9 (2.1–7.7) & 0.77 (0.65–0.84) & 78.3 (68.6–92.1) & 59.1 (46.1–75.4) \\ \hline \hline \end{tabular}
\end{table}
Table 4: PVS segmentation results of the nnU-Net and SynthSeg models on the internal and external test set reported as median (range).  
Figure 5: Comparison of PVS segmentation performance of the nnU-Net and SynthSeg models on the internal test set and external test set. Red cross markers represent outliers (>1.5 interquartile range). DSC: Dice Similarity Coefficient, HD95: Hausdorff distance 95 th percentile, Surface DSC: surface Dice Similarity Coefficient.  
segmented volume was significantly higher in the internal test set compared to the external set (85.5 cm3 [60.1 - 106.7] vs. 59.1 [46.1-75.4], \(p\) < 0.001)  
When comparing the nnU-Net to the SynthSeg model on the internal test set, the nnU-Net showed a significantly higher DSC and surface DSC (DSC = 0.87 [0.82-0.89] vs. 0.80 [0.72-0.83], \(p\) < 0.001; surface DSC = 0.85 [0.76-0.91] vs. 0.73 [0.29-0.77], \(p\) < 0.001). In contrast, no statistically significant differences between metrics were found between the nnU-Net and the SynthSeg model on the external test set.