## 4 Discussion  
The proposed method for automatic MIP image generation using semantic segmentation demonstrated high segmentation accuracy, with a mean DSC exceeding 0.95 and a mean IoU surpassing 0.90. Compared to other studies [38, 39, 40] that employed semantic segmentation for brain images, these results indicate superior performance in delineating relevant anatomical structures while effectively removing unnecessary regions. When specifically comparing our quantitative metrics with recent state-of-the-art models, our approach shows competitive performance. For instance, [PERSON] et al. [38] reported a median DSC of 0.87 for intracranial aneurysms using a UNet architecture, and [PERSON] et al. [39] achieved a median DSC of 0.87 for intracranial aneurysms segmentation. [PERSON] et al. [40], while focused on skull removal rather than vessel segmentation, reported a DSC of 96.1-97.6 for brain extraction tasks. Although our method and these approaches target different structures, our performance (DSC > 0.95, IoU > 0.90) is comparable to the latest reports. This performance advantage is particularly significant considering the complexity of distinguishing intracranial from extracranial vessels, which have similar intensity profiles, unlike the more distinct intensity differences utilized in traditional skull stripping tasks. The high accuracy achieved in this study can be attributed to the choice of DeepLab v3+ [31] as the network model. DeepLab v3+ incorporates atrous convolution and an improved spatial pyramid pooling approach, enhancing its ability to capture fine details at multiple scales. This characteristic is particularly advantageous for segmenting complex anatomical structures, such as intracranial arteries and surrounding tissues, where precise boundary delineation is crucial. The model's ability to distinguish brain parenchyma from high-intensity regions such as fat tissues contributed significantly to the overall segmentation accuracy. The DeepLab v3+ network, a 2D CNN-based model, was employed for semantic segmentation. A 2D approach was chosen to optimize computational efficiency and facilitate data augmentation, including rotation and scaling, which would be more complex in a 3D CNN framework. While 3D CNNs can capture spatial dependencies more effectively, the increased computational cost and the relatively small dataset size made a 2D approach more practical for this study. Unlike studies that concentrate solely on refining the architecture of deep segmentation networks, our work focuses on the clinical workflow surrounding MRA scans. By using DeepLab v3+ to remove non-intracranial arteries, we highlight how an established segmentation method can be leveraged in a software pipeline specifically designed for MRA preprocessing. This includes the DICOM-based import and export of data, as well as precise ROI annotation guidelines for consistent operator-independent tissue removal. Through these contributions, we aim to demonstrate a practical solution that integrates seamlessly into standard neurovascular imaging protocols. To further evaluate the effectiveness of the trained segmentation model, MIP images were generated based on the segmented outputs. As shown in Figure 6, the automatically processed MIP images exhibited clear suppression of external carotid branches, including the superficial temporal artery and middle meningeal artery, which are commonly misrepresented in conventional TOF-MRA MIP images. However, despite the overall success in removing non-intracranial vessels, certain venous structures, such as the occipital venous sinus, were not entirely excluded in some cases. One potential reason for this incomplete removal is that these veins travel within the brain parenchyma, making their signal intensity distribution more variable and less distinguishable from arteries based on conventional segmentation features.