# Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations

arXiv: https://arxiv.org/abs/2203.10517 · HTML: https://mlsanity.com/paper/2203.10517/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence](https://mlsanity.com/paper/2011.13650/) (2020)
- [Neural Cages for Detail-Preserving 3D Deformations](https://mlsanity.com/paper/1912.06395/) (2019)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows](https://mlsanity.com/paper/2007.10973/) (2020)
- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction](https://mlsanity.com/paper/2102.07899/) (2021)
- [DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates](https://mlsanity.com/paper/2102.09105/) (2021)
- [DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image](https://mlsanity.com/paper/1708.04672/) (2017)
- [An attempt at beating the 3D U-Net](https://mlsanity.com/paper/1908.02182/) (2019)
- [3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata](https://mlsanity.com/paper/1907.01913/) (2019)

## Built on by (most cited first, 21 of 21)

- [SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects](https://mlsanity.com/paper/2311.00332/) (2023)
- [Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI](https://mlsanity.com/paper/2311.13706/) (2023)
- [LinFlo-Net: A two-stage deep learning method to generate simulation ready meshes of the heart](https://mlsanity.com/paper/2310.20065/) (2023)
- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants](https://mlsanity.com/paper/2505.21019/) (2025)
- [Probabilistic learning of the Purkinje network from the electrocardiogram](https://mlsanity.com/paper/2312.09887/) (2023)
- [AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis](https://mlsanity.com/paper/2407.00535/) (2024)
- [Image2Flow: A hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data](https://mlsanity.com/paper/2402.18236/) (2024)
- [Robust automated calcification meshing for biomechanical cardiac digital twins](https://mlsanity.com/paper/2403.04998/) (2024)
- [MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction](https://mlsanity.com/paper/2412.10985/) (2024)
- [Relational Anatomical Supervision for Accurate 3D Multi-Chamber Cardiac Mesh Reconstruction](https://mlsanity.com/paper/2503.07874/) (2025)
- [Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning](https://mlsanity.com/paper/2504.19401/) (2025)
- [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins](https://mlsanity.com/paper/2606.01808/) (2026)
- [Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering](https://mlsanity.com/paper/2607.01952/) (2026)
- [An Optimization Framework to Personalize Passive Cardiac Mechanics](https://mlsanity.com/paper/2404.02807/) (2024)
- [Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach](https://mlsanity.com/paper/2505.10902/) (2025)
- [Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing](https://mlsanity.com/paper/2506.08280/) (2025)
- [KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks](https://mlsanity.com/paper/2602.13299/) (2026)
- [HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation](https://mlsanity.com/paper/2607.04243/) (2026)
- [Learning to Beat: Phenotype-Guided Latent Flow with Regional Motion Priors for Biventricular Motion Synthesis](https://mlsanity.com/paper/2608.19738/) (2026)
- [A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model](https://mlsanity.com/paper/2608.19932/) (2026)

## Abstract (arXiv)

Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.