# Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?

arXiv: https://arxiv.org/abs/2605.28697 · HTML: https://mlsanity.com/paper/2605.28697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 18 of 18)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking](https://mlsanity.com/paper/2307.15055/) (2023)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories](https://mlsanity.com/paper/2204.04153/) (2022)
- [Echocardiography Segmentation with Enforced Temporal Consistency](https://mlsanity.com/paper/2112.02102/) (2021)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)
- [EchoTracker: Advancing Myocardial Point Tracking in Echocardiography](https://mlsanity.com/paper/2405.08587/) (2024)
- [SIMUS: an open-source simulator for medical ultrasound imaging. Part I: theory & examples](https://mlsanity.com/paper/2102.02738/) (2021)
- [Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?](https://mlsanity.com/paper/2305.01997/) (2023)
- [Low Complexity Point Tracking of the Myocardium in 2D Echocardiography](https://mlsanity.com/paper/2503.10431/) (2025)
- [Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning](https://mlsanity.com/paper/2406.17902/) (2024)
- [Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning](https://mlsanity.com/paper/2511.02210/) (2025)

## Built on by (most cited first, 1 of 1)

- [Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking](https://mlsanity.com/paper/2609.09577/) (2026)

## Abstract (arXiv)

Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain remains limited, even though this biomarker is highly relevant for early diagnosis and the characterization of subtle abnormalities. from clinical data. Deep learning is a promising alternative, but its development is constrained by the lack of reliable motion references. Existing solutions rely either on STE-derived labels or on simulations generated by physics-based models, but these synthetic sequences still have limited realism compared with clinical data.In this paper, we propose a novel simulation strategy that incorporates speckle decorrelation measures from real videos and uses an iterative refinement process to improve the motion realism in the simulations. We created an open-source photorealistic dataset of 1,478 videos with reference motion, which was used to train an echocardiographic motion estimation algorithm. The proposed method achieves unmatched performance on global and regional strain, notably reaching a GLS variability of 1.42% in an inter-expert setting compared to 1.78% for the clinical reference.