Abstract #2492

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

Pei-Hsuan Kuo¹, Shuu-Jiun Wang^2,3,4, Shih-Pin Chen^2,3,4,5,6, Jiing-Feng Lirng^2,7, Chia-Hung Wu^2,5,7, Yu Kuo^2,7, and Chia-Feng Lu¹

¹Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ²School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ³Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ⁴Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ⁵Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ⁶Division of Translational Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ⁷Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

Synopsis

Keywords: Blood Vessels, Blood vessels

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.

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