## 4 Discussion
Similarly, incorporating additional imaging modalities might benefit certain applications, but it falls outside the scope of this study, which already leverages the most clinically relevant and available imaging modalities for radiotherapy patients. Despite these limitations, the significantly higher clinician ratings in the external test set compared to the SynthSeg model suggest that even in its current form, nnU-Net may offer superior clinical acceptability for automated ventricle segmentation. It is important to note that the nnU-Net was trained on both CT and T1 CE MRI data, and both modalities were used as input during inference. In contrast, the SynthSeg model was trained exclusively on synthetic T1-weighted MRI scans and applied only to the T1 CE MRI data in this study. While this difference reflects the practical limitations of available models, our primary aim was to compare the performance of these models as they would be used in a clinical setting. Training the nnU-Net with CT and MRI mirrors the real-world scenario where both modalities are accessible for treatment planning. The analysis of the local segmentation error demonstrated an overall acceptance of the nnU-Net and SynthSeg segmentations across the ventricle structure. However, in the central region of the left and right temporal and occipital horns of the lateral ventricles, both models showed a low median paired with a high range difference between the automated segmentations and the GT. This pairing of low median difference and high range suggests substantial variability in the segmentation errors of both the nnU-Net and SynthSeg models within the population. The temporal and occipital horns of the lateral ventricles, in particular, presented challenges due to their narrow size which can come close to the voxel resolution, making them difficult to detect on T1 CE MRI images. Because of the sub-voxel dimensions of these structures, voxel-based segmentation methods such as those employed by nnU-Net and SynthSeg may struggle to achieve optimal accuracy. Similar challenges with disconnected tubular structures have been observed in other cranial OARs, such as the optic chiasm and optic nerves. For instance, [PERSON] et al. [30] addressed such disconnections by applying a graph-based post-processing algorithm to enforce anatomical consistency, successfully restoring connectivity between the eye and chiasm. Moreover, [PERSON] et al. [31] proposed the soft centerline Dice (soft-clDice) loss function, which enhances both the connectivity and DSC of tubular structures by promoting topological preservation. Both the graph-based approach by [PERSON] et al. [30] and the soft-clDice loss function by [PERSON] et al. [31] could potentially improve segmentation accuracy and connectivity in the temporal and occipital horn regions of the lateral ventricles. However, if these connections are barely visible or entirely absent on T1 CE MRIs, there may be limited clinical value in enforcing their continuity. To contextualize the performance of the nnU-Net and SynthSeg models in our study, we compared our segmentation results to prior DL studies focused on ventricular segmentation. The reported Dice Similarity Coefficients (DSCs) in these studies range from 0.89 to 0.97. However, direct comparisons are complicated due to differences in patient cohorts, imaging modalities, and segmentation goals. For example, [PERSON] et al. [9; 10] focused on normal pressure hydrocephalus (NPH) patients, whose enlarged ventricles yielded higher contrast and simplified delineation. Their DL models trained on mixed datasets of healthy controls and NPH patients achieved DSCs of 0.97 on internal test sets, with a lower per formance of 0.90 reported in a healthy cohort. However, because both studies included a substantial number of NPH patients in their test sets whose enlarged ventricles are easier to segment and presented a much larger target volume, the DSC scores were automatically skewed toward higher values. Similarly, [PERSON] et al.