## 4 Discussion
Additionally, the anatomical variability in venous structures across individuals may have introduced inconsistencies in the training dataset, thereby reducing the model's ability to generalize effectively to all cases. Currently, the developed tool allows for a single ROI per axial slice, meaning that entire unwanted structures must be included or excluded as a whole. Future improvements could involve allowing multiple ROIs per slice, enabling finer control over segmentation and potentially improving the accuracy of venous structure exclusion. By refining the model to handle such detailed segmentations, it may be possible to enhance its ability to discriminate between veins and arteries based on spatial consistency and flow characteristics. In addition to improvements in segmentation precision, the integration of DICOM-based processing represents a significant advancement. While this study primarily relied on JPEG images for training, a dedicated software tool was developed to directly process DICOM images, apply the segmentation model, and generate MIP images in an automated fashion. The software can take a folder containing DICOM files, convert them into a format suitable for segmentation, apply the trained model, and then integrate the segmentation results back into the DICOM format to produce MIP images. The comparison between MIP images generated from JPEG-based segmentation and those obtained directly from DICOM processing showed strong similarity, indicating that the approach can be feasibly implemented in real-world clinical workflows. Since medical imaging data are natively stored in DICOM format, having the capability to process images without requiring conversion to alternative formats is a crucial advantage for seamless integration into clinical environments. The efficiency of the developed segmentation pipeline was also evaluated in terms of processing speed. The model achieved an average processing speed of 16.61 fps, meaning that approximately 16 axial slices could be segmented per second. Given that a typical brain TOF-MRA scan consists of around 160 slices, it is estimated that the entire set of axial images can be processed and a segmentation-enhanced MIP image can be displayed within approximately 10 s. This significantly reduces the time required compared to manual editing, which typically takes several minutes per case. This reduction in processing time is particularly beneficial in clinical settings where rapid assessment of cerebrovascular structures is essential, such as in emergency cases involving suspected aneurysms or vascular malformations. The ability to generate MIP images with automatically refined segmentation in near real time presents a potential improvement over conventional MIP image review, where non-intracranial structures can obscure diagnostically relevant features. The proposed method enables rapid MIP image generation, significantly reducing the time required for manual editing. While speed improvement is inherently expected with deep learning-based segmentation, our study emphasizes the clinical advantage of automating a traditionally manual preprocessing step. Our segmentation task, while seemingly similar to skull removal, differs in several key aspects. skull removal typically involves extracting the brain by removing the surrounding skull and scalp, which are structurally distinct from the brain and exhibit different intensity profiles in T1-weighted and T2-weighted MRI. In contrast, our approach focuses on selectively removing external carotid arteries, which have high signal intensities similar to intracranial arteries in TOF-MRA images. This makes segmentation more complex, as conventional thresholding-based methods used in skull stripping are not directly applicable. Instead, our model must differentiate between relevant and irrelevant vascular structures based on anatomical context rather than simple intensity differences. Prior research in skull removal,such as the work by [PERSON] et al.