# Modeling 3D cardiac contraction and relaxation with point cloud deformation networks

arXiv: https://arxiv.org/abs/2307.10927 · HTML: https://mlsanity.com/paper/2307.10927/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation](https://mlsanity.com/paper/1712.07262/) (2017)
- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud](https://mlsanity.com/paper/2305.14486/) (2023)
- [Deep Computational Model for the Inference of Ventricular Activation Properties](https://mlsanity.com/paper/2208.04028/) (2022)
- [One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud](https://mlsanity.com/paper/1907.10763/) (2019)
- [Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning](https://mlsanity.com/paper/2209.02004/) (2022)
- [Can point cloud networks learn statistical shape models of anatomies?](https://mlsanity.com/paper/2305.05610/) (2023)
- [Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI](https://mlsanity.com/paper/1907.13524/) (2019)
- [3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks](https://mlsanity.com/paper/2307.07298/) (2023)

## Built on by (most cited first, 7 of 7)

- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation](https://mlsanity.com/paper/2508.03057/) (2025)
- [3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models](https://mlsanity.com/paper/2508.14122/) (2025)
- [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)
- [MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI](https://mlsanity.com/paper/2507.17678/) (2025)
- [Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins](https://mlsanity.com/paper/2512.03055/) (2025)
- [Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI](https://mlsanity.com/paper/2609.29825/) (2026)

## Abstract (arXiv)

Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation process and hence, limit the understanding of both healthy and pathological cardiac mechanics. In this work, we propose the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach to model 3D cardiac contraction and relaxation between the extreme ends of the cardiac cycle. It employs the recent advances in point cloud-based deep learning into an encoder-decoder structure, in order to enable efficient multi-scale feature learning directly on multi-class 3D point cloud representations of the cardiac anatomy. We evaluate our approach on a large dataset of over 10,000 cases from the UK Biobank study and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. Furthermore, we observe similar clinical metrics between predicted and ground truth populations and show that the PCD-Net can successfully capture subpopulation-specific differences between normal subjects and myocardial infarction (MI) patients. We then demonstrate that the learned 3D deformation patterns outperform multiple clinical benchmarks by 13% and 7% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.