{"ID":2874920,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03095","arxiv_id":"2509.03095","title":"TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis","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.","short_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 datas...","url_abs":"https://arxiv.org/abs/2509.03095","url_pdf":"https://arxiv.org/pdf/2509.03095v1","authors":"[\"Clément Hervé\",\"Paul Garnier\",\"Jonathan Viquerat\",\"Elie Hachem\"]","published":"2025-09-03T07:51:17Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}