## 4 Discussion  
This study developed a DL model to segment the ventricles and PVS automatically, following EPTN guidelines on CT and T1 CE MRI in a neuro-oncological patient population. The model's performance was then assessed against an off-the-shelf segmentation model. We observe that in terms of segmentation metrics, both models seemed to perform better on the internal test set compared to the external test set. This may be expected from the nnU-Net, as both the training and internal test set were sourced from the same patient population and delineated by the same two individuals. For the SynthSeg model, it was surprising that it performed significantly better in terms of the surface DSC and APL on the internal test set compared to the external test set, as it was originally trained on a different dataset, and no preference was expected. The clinical rating shows no significant differences in segmentation quality between the nnU-Net and SynthSeg models across internal and external test sets. However, discrepancies between the clinical ratings and segmentation metrics are well documented [26-28], [PERSON] et al. [29] found that the DSC, HD, and surface DSC had only moderate correlation with clinical ratings, with HD showing particularly weak, and sometimes even negative, correlation. These findings highlight the need for new segmentation metrics with better correlation to clinical ratings. Despite following the same EPTN guidelines, the clinical rating revealed significant differences in the GT delineations between the two test sets. While these guidelines help standardize OAR delineation and reduce interobserver variability, studies show they do not eliminate it entirely [4]. Within the internal test set, the nnU-Net outperformed the SynthSeg model across all four segmentation metrics. Notably, the nnU-Net's significantly better surface DSC and APL scores on the internal test set emphasize its advantage over SynthSeg, as they are linked to clinical time savings in delineation [20]. Additionally, clinician ratings significantly favored the nnU-Net segmentations over the SynthSeg segmentations on the internal test set, suggesting greater clinical acceptability. In contrast, no significant differences in segmentation metrics were observed between the models on the external test set, though clinician ratings again favored the nnU-Net segmentation, significantly surpassing both the SynthSeg and even GT segmentations. Similarly, previous studies [28,29] found that clinical ratings of automated CNN segmentations were consistently higher than those of their expert-created reference labels. These findings highlight that it is important to reflect upon whether the human reference labels can qualify as a GT while also highlighting the need to develop better annotation procedures. In addition, it suggests that autosegmentations have the potential to reduce interobserver variability. The clinical ratings suggest that nnU-Net segmentations may hold higher clinical value in both an internal and external setting while also pointing to possible systematic differences in GT delineation between internal and external test sets. The lack of significant segmentation metric differences between both models in the external set may also be due to the relatively modest training set size for nnU-Net (n = 78), which may limit its ability to generalize across institutions. Additionally, both the internal (n = 18) and external (n = 18) test sets are relatively small, which may constrain the statistical power and generalizability of the findings. While the SynthSeg model was trained on a considerably larger, but synthetic, dataset (N = 1020), expanding the nnU-Net training data with additional multicenter samples could enhance its robustness and external validity. However, creating multicenter datasets for this purpose presents significant logistical challenge.