# Learning correspondences of cardiac motion from images using biomechanics-informed modeling

arXiv: https://arxiv.org/abs/2209.00726 · HTML: https://mlsanity.com/paper/2209.00726/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration](https://mlsanity.com/paper/1805.04605/) (2018)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2105.07059/) (2021)
- [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022)
- [Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation](https://mlsanity.com/paper/2009.02831/) (2020)
- [Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation](https://mlsanity.com/paper/2206.02307/) (2022)
- [Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI](https://mlsanity.com/paper/2006.04725/) (2020)
- [Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation](https://mlsanity.com/paper/2206.01369/) (2022)
- [Fully Automated Left Atrium Segmentation from Anatomical Cine Long-axis MRI Sequences using Deep Convolutional Neural Network with Unscented Kalman Filter](https://mlsanity.com/paper/2009.13627/) (2020)
- [Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences](https://mlsanity.com/paper/2008.07770/) (2020)

## Built on by (most cited first, 8 of 8)

- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [4D Myocardium Reconstruction with Decoupled Motion and Shape Model](https://mlsanity.com/paper/2308.14083/) (2023)
- [Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration](https://mlsanity.com/paper/2312.00836/) (2023)
- [Adaptive Correspondence Scoring for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2312.00837/) (2023)
- [LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation](https://mlsanity.com/paper/2407.02229/) (2024)
- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI using Online Learning Neural Networks](https://mlsanity.com/paper/2312.09387/) (2023)
- [From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification](https://mlsanity.com/paper/2507.05783/) (2025)

## Abstract (arXiv)

Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the L2 norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated datasets. In this work, we propose an explicit biomechanics-informed prior as regularization on the predicted DVF in modeling a more generic biomechanically plausible transformation within all cardiac structures without introducing additional training complexity. We validate our methods on two publicly available datasets in the context of 2D MRI data and perform extensive experiments to illustrate the effectiveness and robustness of our proposed methods compared to other competing regularization schemes. Our proposed methods better preserve biomechanical properties by visual assessment and show advantages in segmentation performance using quantitative evaluation metrics. The code is publicly available at urlhttps://github.com/Voldemort108X/bioinformedreg.