# Low Complexity Point Tracking of the Myocardium in 2D Echocardiography

arXiv: https://arxiv.org/abs/2503.10431 · HTML: https://mlsanity.com/paper/2503.10431/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 13 Mar 2025.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [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)
- [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)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [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)
- [EchoTracker: Advancing Myocardial Point Tracking in Echocardiography](https://mlsanity.com/paper/2405.08587/) (2024)

## Built on by (most cited first, 5 of 5)

- [Taming Modern Point Tracking for Speckle Tracking Echocardiography via Impartial Motion](https://mlsanity.com/paper/2507.10127/) (2025)
- [Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?](https://mlsanity.com/paper/2605.28697/) (2026)
- [Anatomically Constrained Transformers for Echocardiogram Analysis](https://mlsanity.com/paper/2511.01109/) (2025)
- [EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion](https://mlsanity.com/paper/2605.12140/) (2026)
- [Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking](https://mlsanity.com/paper/2609.09577/) (2026)

## Abstract (arXiv)

Deep learning methods for point tracking are applicable in 2D echocardiography, but do not yet take advantage of domain specifics that enable extremely fast and efficient configurations. We developed MyoTracker, a low-complexity architecture (0.3M parameters) for point tracking in echocardiography. It builds on the CoTracker2 architecture by simplifying its components and extending the temporal context to provide point predictions for the entire sequence in a single step. We applied MyoTracker to the right ventricular (RV) myocardium in RV-focused recordings and compared the results with those of CoTracker2 and EchoTracker, another specialized point tracking architecture for echocardiography. MyoTracker achieved the lowest average point trajectory error at 2.00 ± 0.53 mm. Calculating RV Free Wall Strain (RV FWS) using MyoTracker's point predictions resulted in a -0.3% bias with 95% limits of agreement from -6.1% to 5.4% compared to reference values from commercial software. This range falls within the interobserver variability reported in previous studies. The limits of agreement were wider for both CoTracker2 and EchoTracker, worse than the interobserver variability. At inference, MyoTracker used 67% less GPU memory than CoTracker2 and 84% less than EchoTracker on large sequences (100 frames). MyoTracker was 74 times faster during inference than CoTracker2 and 11 times faster than EchoTracker with our setup. Maintaining the entire sequence in the temporal context was the greatest contributor to MyoTracker's accuracy. Slight additional gains can be made by re-enabling iterative refinement, at the cost of longer processing time.