TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis

2025/09/03 by Clément Hervé, Hervé, Clément, Paul Garnier +5 · 1 citation
Computer Science · Engineering · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Intracranial Aneurysms: Treatment and Complications #Machine Learning (cs.LG) #Medical Image Segmentation Techniques

paper · pdf · doi:10.48550/arxiv.2509.03095

openalex publication_date 2025/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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Cited by

• Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment

Related

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• A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks
• Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning