## 2 Materials and Methods
### Semantic Segmentation for Automatic MIP Image Generation  
In this study, we employed DeepLab v3+ [31] as our semantic segmentation framework for automatic MIP image generation. DeepLab v3+ was selected based on its ability to capture multi-scale contextual information through atrous spatial pyramid pooling, as well as its strong performance in medical image segmentation tasks [32]. Compared with other architectures, such as U-Net [33] or a fully 3D convolutional network [34], DeepLab v3+ offers a favorable balance between segmentation accuracy and computational efficiency [35; 36; 37]. For instance, U-Net-based methods are widely used but can sometimes lose fine vessel boundaries, particularly in small datasets, while 3D CNNs can better capture volumetric features but often require significantly more computational resources and larger training sets. An in-house software was developed in MATLAB (v2024b; The MathWorks, Inc., Natick, MA, USA) to allow manual creation of Regions of Interest (ROIs) on the axial images. The manual segmentation process was performed by a radiological technologist ([PERSON]). The annotation criteria were as follows: at the basal ganglia level, external carotid artery branches and non-intracranial structures were excluded; at the orbital level, fat tissue around the optic nerves was removed to reduce bright signal contamination; at the temporal-occipital junction level, only the internal carotid artery and vertebhosimilar system were retained. Each slice was independently reviewed by three experienced radiological technologists (Ta.Y., S.I. and H.S.) to ensure accuracy and consistency. Using this tool, the operator could visually identify and outline regions containing only the relevant intracranial vessels, excluding tissue and vascular structures deemed unnecessary for the final MIP representation. The 20 sets of axial images were thus annotated by drawing ROIs on each slice, guided by anatomical knowledge and specific exclusion criteria for unwanted structures.  
To ensure clarity, ROI creation was tailored to different slice levels, which broadly corresponded to the cranial anatomy from the basal ganglia level of the head down to the skull base. These anatomical levels were selected to systematically exclude non-intracranial structures while preserving the integrity of the cerebrovascular network, ensuring that all critical intracranial vessels were retained while minimizing interference from external carotid branches. Representative examples (Figures 2-5) illustrate how the ROIs were manually delineated.  
Figure 1: MIP images from different views and arteries that hinder the evaluation of the internal carotid system. (**a**) Sagittal MIP image, (**b**) coronal MIP image, and (**c**) axial MIP image. Yellow arrow: superficial temporal artery, red arrow: middle meningeal artery, and light blue arrow: occipital artery.  
Figure 4: Example of manually annotated training data at the temporal-occipital junction level. (**a**) Original TOF-MRA axial image (inside red line indicates internal carotid arteries and vertebral arteries). (**b**) Manually defined region of interest (ROI) in blue, excluding the external carotid artery and non-intracranial structures. (**c**) Corresponding indexPNG mask for semantic segmentation training.  
Figure 3: Example of manually annotated training data at the orbital level. (**a**) Original TOF-MRA axial image (red line indicates the line connecting both optic nerves’ endpoints). (**b**) Manually defined region of interest (ROI) in blue, excluding the external carotid artery and non-intracranial structures. (**c**) Corresponding indexPNG mask for semantic segmentation training.  
Figure 2: Example of manually annotated training data at the basal ganglia level. (**a**) Original TOF-MRA axial image (red circles indicate the superficial temporal artery). (**b**) Manually defined region of interest (ROI) in blue, excluding the external carotid artery and non-intracranial structures. (**c**) Corresponding indexPNG mask for semantic segmentation training.