Accurate intracranial aneurysm assessment is essential for treatment planning and risk stratification. Manual aneurysm segmentation is labor-intensive and subject to substantial inter- and intra-observer variability. Although automated segmentation approaches have been proposed, many suffer from limited accuracy, lack of robustness across datasets, or insufficient validation on heterogeneous, real-world data. As a result, reliable and generalizable tools for aneurysm segmentation and morphological analysis remain an unmet need. DIVA-seg, an nnU-Net-based model, achieved high aneurysm segmentation accuracy (DSC >0.86; HD <0.7mm) and close agreement with expert annotations in clinically relevant 3D morphological measures, demonstrating consistent performance across internal and external datasets. This work demonstrates a robust and generalizable approach for automated intracranial aneurysm segmentation, enabling reliable morphological analysis. The proposed method has the potential to streamline aneurysm monitoring, reduce observer variability, and support future automated tools for risk predictions and clinical decision making.
Verschuur et al. (Sat,) studied this question.