###### Abstract  
Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis.  
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Footnote †: journal: Applied Sciences  
article  
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or fusiform in nature, and large aneurysms exceeding approximately 10-25 mm are at a notably higher risk of rupture leading to subarachnoid hemorrhage [1]. Subarachnoid hemorrhage is well known for its high mortality rate, and even in cases where patients survive the initial bleed, complications such as cerebral vasospasm, brain atrophy, and hydrocephalus can lead to significant morbidity. As a result, the likelihood of a favorable outcome following rupture remains only around 30-40%, posing a considerable threat to long-term quality of life.  
Early detection [2] is therefore paramount for preventing rupture, but the challenge lies in the fact that most unruptured cerebral aneurysms remain asymptomatic, making them difficult to detect until they are incidentally found or until a catastrophic event occurs [3]. For many years, digital subtraction angiography (DSA) has served as a gold-standard imaging modality due to its high diagnostic accuracy and spatial resolution. However, it carries risks such as rebleeding during the procedure or the generation of micro-emboli, prompting a shift toward employing DSA primarily in therapeutic interventions and confirming final treatment decisions.  
Meanwhile, less invasive techniques have been embraced for screening purposes, particularly CT angiography (CTA) and MR angiography (MRA). Of these, MRA is especially attractive in routine brain examinations because it does not require contrast agents, providing a noninvasive approach ideal for widespread screening. MRA is thus frequently used in brain check-up programs to identify potential intracranial aneurysms in asymptomatic individuals. Nevertheless, a known limitation is its lower spatial resolution relative to CTA, which complicates the detection of smaller aneurysms. Indeed, a past study has reported [4] that for aneurysms measuring 5 mm or less, the diagnostic yield is only about 80%, and for lesions of 2 mm or below, accurate identification becomes remarkably difficult. This underscores the critical need for improved detection and the reduction of missed diagnoses, particularly in smaller or more complex aneurysm morphologies.