ABSTRACT Accurate classification and segmentation of intracranial aneurysms from 3D point cloud data are critical for computer‐aided diagnosis and surgical planning. However, existing point‐based deep learning methods suffer from limited feature representation and poor segmentation performance on medical data due to insufficient training samples and complex geometric variations. M‐PointNet introduces a novel multi‐layer embedded deep learning architecture that significantly enhances the classification and segmentation of intracranial aneurysms through three key innovations: (1) an enhanced PointNet++ with an expanded hierarchical structure for better geometric feature extraction; (2) a multi‐layer embedding mechanism that integrates preprocessed and resampled point cloud data at multiple hierarchical levels to enrich feature representation; and (3) a deep supervision strategy with auxiliary output layers to accelerate convergence and improve performance. Experiments on the IntrA dataset demonstrate that M‐PointNet achieves 91.96% accuracy and a 0.923 F1‐score in classification, surpassing baseline by 5.27% and 3.0%, respectively. For segmentation, it attains 83.85% IoU and 90.25% DSC for aneurysm regions and 95.81% IoU and 97.82% DSC for vessel regions. Additionally, its generalization capability is validated by a 92.8% accuracy on the ModelNet40 dataset. M‐PointNet effectively addresses the challenges of medical point cloud analysis, achieving state‐of‐the‐art performance in intracranial aneurysms classification and segmentation while maintaining robust cross‐domain generalization.
Wang et al. (2026) studied this question.