用通用3D生成模型特征提升脑动脉瘤分析精度
TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis
- 用TRELLIS生成的表面特征替代传统法向量，增强神经网络表征能力
- 在三个任务中均显著提升准确率与分割质量，血流模拟误差降15%
- 适合医学图像分析、3D深度学习研究者参考
颅内动脉瘤临床风险高，但因缺乏标注的3D数据，检测、分割和建模困难。本文提出一种跨域特征迁移方法，利用在大规模非医疗3D数据上训练的生成模型TRELLIS所学习到的隐式几何嵌入，增强神经网络对动脉瘤的分析能力。通过将传统的点法向量或网格描述符替换为TRELLIS表面特征，系统性提升三个下游任务：(i) Intra3D数据集上动脉瘤与健康血管的分类；(ii) 3D网格上的动脉瘤与血管区域分割；(iii) 基于图神经网络在AnXplore数据集上预测随时间演化的血流场。实验表明，引入这些特征后，在准确率、F1分数和分割质量上均优于当前最优基线，且模拟误差降低15%。结果展示了将通用生成模型的3D表征迁移到特定医学任务中的广阔潜力。
原文摘要 · Abstract (English)
Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate and model due to limited annotated 3D data. We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on large-scale non-medical 3D datasets, to augment neural networks for aneurysm analysis. By replacing conventional point normals or mesh descriptors with TRELLIS surface features, we systematically enhance three downstream tasks: (i) classifying aneurysms versus healthy vessels in the Intra3D dataset, (ii) segmenting aneurysm and vessel regions on 3D meshes, and (iii) predicting time-evolving blood-flow fields using a graph neural network on the AnXplore dataset. Our experiments show that the inclusion of these features yields strong gains in accuracy, F1-score and segmentation quality over state-of-the-art baselines, and reduces simulation error by 15\%. These results illustrate the broader potential of transferring 3D representations from general-purpose generative models to specialized medical tasks.
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