{"ID":5937668,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T11:34:04.318834241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04243","arxiv_id":"2607.04243","title":"HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation","abstract":"Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limited cross-case correspondence. We propose HeartVolMesh, which lifts each template vertex to an anisotropic Gaussian kernel and uses a 3D CNN-GNN to predict per-vertex displacements and Cholesky-parameterized covariances from volumetric images. Training is guided by a covariance-aware negative log-likelihood loss with lightweight mesh regularization. For volumetric meshing, we warp a fixed tetrahedral template to the reconstructed surface via staged alignment, non-rigid registration, and deformation propagation, preserving connectivity and correspondence by construction, with resolution controlled by template density. Experiments show consistent gains over deformation-based baselines in surface mesh accuracy and volumetric mesh fidelity.","short_abstract":"Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limited cross-case correspondence. We propose HeartVolMesh, which lifts each template vertex to an anisotropic Gaussian kernel and uses a 3D CNN-...","url_abs":"https://arxiv.org/abs/2607.04243","url_pdf":"https://arxiv.org/pdf/2607.04243v1","authors":"[\"Fengming Lin\",\"Arezoo Zakeri\",\"Haoran Dou\",\"Zherui Zhou\",\"Shaokun Lan\",\"Jinming Duan\",\"Alejandro Frangi\"]","published":"2026-07-05T11:42:37Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Convolutional Neural Network\",\"Graph Neural Network\"]","has_code":false}