# DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning arXiv: https://arxiv.org/abs/2309.14306 ยท HTML: https://mlsanity.com/paper/2309.14306/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 56 references that are in the map. - First posted on arXiv 25 Sep 2023. ## Builds on (most cited first, 23 of 23) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016) - [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015) - [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018) - [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018) - [Learning Category-Specific Mesh Reconstruction from Image Collections](https://mlsanity.com/paper/1803.07549/) (2018) - [Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks](https://mlsanity.com/paper/1909.00321/) (2019) - [Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows](https://mlsanity.com/paper/2007.10973/) (2020) - [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018) - [Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow](https://mlsanity.com/paper/2203.08652/) (2022) - [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018) - [DARNet: Deep Active Ray Network for Building Segmentation](https://mlsanity.com/paper/1905.05889/) (2019) - [DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images](https://mlsanity.com/paper/2103.02772/) (2021) - [Locating Objects Without Bounding Boxes](https://mlsanity.com/paper/1806.07564/) (2018) - [Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration](https://mlsanity.com/paper/2007.02790/) (2020) - [Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI](https://mlsanity.com/paper/2006.04725/) (2020) - [FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation](https://mlsanity.com/paper/2003.04492/) (2020) - [Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow](https://mlsanity.com/paper/1811.03433/) (2018) - [MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI](https://mlsanity.com/paper/2208.00034/) (2022) - [End to End Trainable Active Contours via Differentiable Rendering](https://mlsanity.com/paper/1912.00367/) (2019) - [Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation](https://mlsanity.com/paper/2006.15710/) (2020) - [Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning](https://mlsanity.com/paper/2209.02004/) (2022) - [Enhanced 3D Myocardial Strain Estimation from Multi-View 2D CMR Imaging](https://mlsanity.com/paper/2009.12466/) (2020) ## Built on by (most cited first, 14 of 14) - [Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI](https://mlsanity.com/paper/2311.13706/) (2023) - [CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching](https://mlsanity.com/paper/2509.05754/) (2025) - [Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction](https://mlsanity.com/paper/2409.02070/) (2024) - [End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI](https://mlsanity.com/paper/2509.12090/) (2025) - [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025) - [CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI](https://mlsanity.com/paper/2605.13994/) (2026) - [MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction](https://mlsanity.com/paper/2412.10985/) (2024) - [Parametric shape models for vessels learned from segmentations via differentiable voxelization](https://mlsanity.com/paper/2507.02576/) (2025) - [Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation](https://mlsanity.com/paper/2507.16608/) (2025) - [CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Network](https://mlsanity.com/paper/2508.20734/) (2025) - [VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets](https://mlsanity.com/paper/2604.19403/) (2026) - [NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views](https://mlsanity.com/paper/2608.00752/) (2026) - [MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI](https://mlsanity.com/paper/2507.17678/) (2025) - [Non-Intrusive Parametrized-Background Data-Weak Reconstruction of Cardiac Displacement Fields from Sparse MRI-like Observations](https://mlsanity.com/paper/2509.14844/) (2025) ## Abstract (arXiv) 3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.