Deep Learning-based Automated Vessel Extraction for VR Image Creation From Cerebral TOF-MRA

Accurate and efficient three-dimensional visualization of cerebral vasculature is essential for clinical evaluation; however, manual vessel extraction from time-of-flight (TOF) magnetic resonance angiography angiography (MRA) is time-consuming and operator-dependent. This study aimed to develop a deep learning-based cerebrovascular segmentation model and an automated vessel extraction method, and to evaluate their accuracy, volumetric reliability, and impact on volume rendering (VR) workflow efficiency.

A 3D U-Net-based vessel segmentation model was trained using TOF-MRA images. Automated vessel extraction was performed by dilating predicted vessel regions by one voxel. Forty-eight intracranial aneurysm cases were analyzed. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), normalized surface Dice (NSD); tolerance = 1 mm), and centerline distance (CLD). Inter-rater reliability was assessed using DSC between independently generated vessel masks in a subset of the dataset. Aneurysm volumes from original and vessel-extracted images were compared using equivalence testing with a 1% margin and two one-sided tests (TOST). VR image creation time was measured by 12 radiological technologists.

The DSC between independently generated vessel masks was 0.916. The DSC, recall, and precision of dilated vessel masks were significantly higher than those of non-dilated masks (p < 0.0001). The NSD was 0.982 ± 0.015, and the CLD was 0.196 ± 0.182 mm. Aneurysm volumes showed strong correlation (r = 0.999) with a small mean absolute error (MAE) (0.0915 mm³), and equivalence by TOST (p < 0.001). VR image creation time was significantly reduced (p = 0.0130).

The proposed method enables accurate, reproducible, and time-efficient generation of cerebral vascular VR images, suggesting its potential utility in clinical practice.