TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis

• URL: http://arxiv.org/abs/2509.03095v1
• Date: Wed, 03 Sep 2025 07:51:17 GMT
• Title: TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis
• Authors: Clément Hervé, Paul Garnier, Jonathan Viquerat, Elie Hachem,
• Abstract summary: Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate and model due to limited annotated 3D data.<n>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.
• Score: 2.624902795082451
• License: http://creativecommons.org/licenses/by/4.0/
• 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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