ArticleMedical image analysis2021
A deep-learning approach for direct whole-heart mesh reconstruction.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- GDLS-D: two-stage medical surface reconstruction via geometric diffusion least squares and iterative deformation optimization.Medical & biological engineering & computing · 2026Article
- Neural implicit heart coordinates: 3D cardiac shape reconstruction from sparse segmentations.Medical image analysis · 2026Article
- Feasibility of whole heart 3D shape reconstruction from sparse echocardiographic views using CT-derived simulations.Scientific reports · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Geometric-topological deep transfer learning for precise vessel segmentation in 3D medical volumes.NPJ digital medicine · 2026Article
- Novel noninvasive assessment of upper urinary tract urine flow dynamics: a deep learning-driven reconstruction model combined with CFD simulation.European journal of medical research · 2025Article
- SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations.IEEE transactions on medical imaging · 2025Article
- ToPoMesh: accurate 3D surface reconstruction from CT volumetric data via topology modification.Medical & biological engineering & computing · 2025Article
- Article
- AI-powered automated model construction for patient-specific CFD simulations of aortic flows.Science advances · 2025Article
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.International journal of computer assisted radiology and surgery · 2025Article
- SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations.bioRxiv : the preprint server for biology · 2025Article
- Article
- Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy.Sensors (Basel, Switzerland) · 2025Article
- SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.Annals of biomedical engineering · 2025Article
- Cardiac digital twins at scale from MRI: Open tools and representative models from ~ 55000 UK Biobank participants.PloS one · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating whole heart reconstructions with as good or better accuracy than prior deep-learning-based methods on both CT and MR data. Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data. Our method was also able to produce temporally-consistent surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics. Our code and pre-trained networks are available at https://github.com/fkong7/MeshDeformNet.
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