### Deep Learning for Medical Image Processing and the Need for Automated Preprocessing  
In generating MRA images suitable for diagnosis, it is common to employ the maximum intensity projection (MIP) method, which reconstructs a three-dimensional view of the cerebrovascular structures from axial MRI scans by projecting the maximum signal values along various viewing angles. Although MRA-MIP images are valuable for clinical interpretation, they often include unwanted tissues or vascular structures such as external carotid arteries or surrounding fat tissue. As a result, manual editing is frequently performed to remove irrelevant anatomical features before the final images are ready for diagnostic review. This manual workflow can be time-consuming, leading to a delay in making images available for definitive interpretation. Against this backdrop, recent advances in deep learning have opened up new possibilities for automated image analysis in various fields [5], including medical imaging [6; 7; 8; 9; 10; 11; 12; 13], and have especially shown promise in the domain of neurosurgery [14]. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in image classification tasks, object detection, and semantic segmentation [15], enabling highly accurate identification and localization of critical anatomical structures or pathological regions in brain scans. Among these applications [16; 17], segmentation techniques have been frequently employed in the detection and delineation of intracranial tumors, such as gliomas or meningiomas, because precise boundary estimation is crucial for treatment planning and surgical decision-making. Research in this area has leveraged large datasets and sophisticated network architectures to automatically distinguish tumorous tissue from healthy brain parenchyma, effectively reducing the workload of radiologists and improving the consistency of diagnostic outcomes. Despite these successes, deep learning-based methodologies have not been broadly applied to what could be termed the \"preprocessing stage\" of medical image analysis. In typical clinical workflows, especially in MRI-based examinations, clinicians or radiographers often perform manual preprocessing tasks--such as excluding irrelevant tissues, removing background structures like the scalp or external carotid arteries, and adjusting contrast or brightness--to create images more suitable for further diagnostic interpretation. This manual approach is deeply ingrained in standard practice, largely because it has been the conventional method for ensuring that only pertinent anatomical regions are emphasized, and any extraneous data are eliminated. However, the very nature of such manual preprocessing implies variability in quality and outcome, as the level of precision may differ significantly among operators with different expertise and experience. Moreover, manual editing is time-consuming, adding to the overall waiting period before final diagnostic images can be reviewed by attending physicians or surgeons. As the demand for faster, more accurate imaging in neurosurgical procedures continues to rise, the potential benefits of automating these preliminary steps become increasingly evident. Yet, compared with tumor segmentation or lesion classification--fields where deep learning has made substantial inroads--the challenge of automating preprocessing has not garnered the same level of attention. Several factors might contribute to this gap. First, the datasets required to develop reliable preprocessing algorithms must include a variety of normal and pathological cases to ensure robust performance across different clinical scenarios, but such datasets are often harder to compile and annotate exhaustively for preprocessing purposes than for more targeted tasks like tumor detection.