Brain vessel segmentation plays a central role in many clinical and research applications, including the diagnosis and monitoring of cerebrovascular diseases, the planning of surgical or endovascular interventions, and the quantitative study of vascular anatomy and pathology. Accurate segmentation of the vascular network is therefore of great importance, yet it remains a particularly challenging task. The thin, tortuous, and highly variable morphology of vessels makes their delineation difficult, and even small errors can have significant clinical consequences. While deep learning and advanced image processing techniques have achieved promising results, the automated evaluation of segmentation quality is still underdeveloped. Reliable quality control (QC) methods are essential to ensure the trustworthiness and applicability of vessel segmentation in both research and clinical practice.