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arXiv · 2509.03095

TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis

Clément Hervé·Paul Garnier·Jonathan Viquerat·Elie Hachem
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Abstract

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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Clément Hervé, Paul Garnier, Jonathan Viquerat, Elie Hachem. 2025-09-03. TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis. https://arxiv.org/abs/2509.03095

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