Motivation: Intracranial vessels exhibit significant variations stemming from anatomical distinctions and pathological conditions; therefore, automated vessel labeling is challenging. Goal(s): We aimed to investigate the performance of automated intracranial vessel labeling for multiple cerebrovascular conditions, including normal structure, severe stenosis, occlusion, aging, and calcification. Approach: We developed an automated vessel labeling model solely based on the dataset with normal structures (202 real cases) and evaluated its labeling performance in different cerebrovascular conditions (50 real and 200 simulated cases). Results: The proposed model showed high generalization across cerebrovascular conditions with an average labeling accuracy of 0.82, which could facilitate future quantitative analysis of vessel anomalies. Impact: This study contributes to the application of automated intracranial vessel labeling in different cerebrovascular conditions and offers insights into the model applications in future quantitative analysis for the diagnosis and treatment of vessel anomalies.

Development of automated vessel labeling for multiple cerebrovascular conditions based on magnetic resonance angiography