# Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI arXiv: https://arxiv.org/abs/2311.13706 ยท HTML: https://mlsanity.com/paper/2311.13706/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 46 references that are in the map. - First posted on arXiv 22 Nov 2023. ## Builds on (most cited first, 20 of 20) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017) - [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019) - [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016) - [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016) - [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020) - [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018) - [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014) - [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018) - [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018) - [Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders](https://mlsanity.com/paper/2006.13791/) (2020) - [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022) - [Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations](https://mlsanity.com/paper/2203.10517/) (2022) - [DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning](https://mlsanity.com/paper/2309.14306/) (2023) - [Probabilistic 3D surface reconstruction from sparse MRI information](https://mlsanity.com/paper/2010.02041/) (2020) - [Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations](https://mlsanity.com/paper/2107.10839/) (2021) - [Hybrid graph convolutional neural networks for landmark-based anatomical segmentation](https://mlsanity.com/paper/2106.09832/) (2021) ## Built on by (most cited first, 12 of 12) - [A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics](https://mlsanity.com/paper/2409.13825/) (2024) - [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) - [CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI](https://mlsanity.com/paper/2605.13994/) (2026) - [From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2510.01296/) (2025) - [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025) - [Relational Anatomical Supervision for Accurate 3D Multi-Chamber Cardiac Mesh Reconstruction](https://mlsanity.com/paper/2503.07874/) (2025) - [Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision](https://mlsanity.com/paper/2602.21179/) (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) - [Fitting Skeletal Models via Graph-based Learning](https://mlsanity.com/paper/2409.05311/) (2024) - [MRI-Guided Reslice-Refined Cross-Slice SDF Reconstruction of the Left Ventricle from Cardiac MRI with Sparse Axial Supervision](https://mlsanity.com/paper/2609.08148/) (2026) - [Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI](https://mlsanity.com/paper/2609.29825/) (2026) ## Abstract (arXiv) Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images and then reconstructing meshes, making them time-consuming and prone to error propagation. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multi-view HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity meshes from CMR images. Multi-view HybridVNet outperforms the state-of-the-art, achieving improvements of up to ~27% reduction in Mean Contour Distance (from 1.86 mm to 1.35 mm for the LV Myocardium), up to ~18% improvement in Hausdorff distance (from 4.74 mm to 3.89mm, for the LV Endocardium), and up to ~8% in Dice Coefficient (from 0.78 to 0.84, for the LV Myocardium), highlighting its superior accuracy.