## 4 Discussion
[40], demonstrates the effectiveness of deep learning-based approaches for brain extraction, and similar methodologies can be adapted for vascular segmentation. However, due to the fundamental differences in anatomical structures and segmentation challenges, our task requires a different approach, emphasizing precise vessel boundary delineation rather than complete organ extraction. Future work may explore integrating vessel topology information to further refine segmentation performance. Despite these promising results, several limitations must be acknowledged. One notable limitation is related to the consistency of intensity variations in the training dataset. During the acquisition of TOF-MRA images, variations in signal intensity across slices were observed, likely due to differences in scanner parameters, patient movement, and inherent variations in flow dynamics. These inconsistencies may have affected segmentation performance, particularly in cases where signal intensity discontinuities led to misclassification of vessel structures. One potential approach to mitigating this issue is to incorporate data augmentation strategies that introduce controlled intensity variations during training. By exposing the model to a wider range of intensity profiles, it may become more robust to natural variations present in clinical datasets. Another limitation observed in the evaluation was the variance in segmentation accuracy across different anatomical levels. When observing DSC and IoU scores on a per-slice basis, segmentation accuracy was found to be consistently higher at the basal ganglia and orbital levels but lower at the skull base level. This discrepancy can be explained by differences in anatomical complexity between these regions. The basal ganglia level primarily consists of relatively large intracranial arteries with clear boundaries, whereas the skull base region features more intricate vessel networks, including smaller arterial branches and venous structures that may have been misclassified. These findings suggest that a single segmentation model may not be optimal for all anatomical regions. Future work could involve training multiple models specialized for different anatomical levels, with tailored preprocessing and feature extraction techniques for each region. By employing region-specific segmentation models, it may be possible to further enhance segmentation accuracy, particularly in challenging areas such as the skull base. A basic limitation of this study is the relatively small dataset size (n = 20). To mitigate this issue, we employed extensive data augmentation strategies, including rotation (\(-25^{\circ}\) to \(+25^{\circ}\)) and scaling (0.7\(\times\) to 1.0\(\times\)), as well as a 5-fold cross-validation approach. While these measures enhance generalizability, future studies should incorporate larger and more diverse datasets to further validate the robustness of our model. Additionally, expanding the dataset to include images acquired with different scanners and imaging protocols will be crucial to assessing the generalization ability of the proposed segmentation method. Another aspect that warrants further investigation is the generalizability of the developed segmentation model to datasets acquired from different scanners and imaging protocols. Although the model performed well on the dataset used in this study, variations in MRI acquisition parameters, such as slice thickness, magnetic field strength, and sequence settings, could potentially affect segmentation performance. To address this limitation, additional datasets should be incorporated into the training process, covering a diverse range of imaging conditions. This would allow the model to learn more robust representations and improve its ability to generalize across different imaging environments. Moreover, while the current study focused on vessel segmentation, the method could potentially be extended to other applications within neurovascular imaging.