# EchoTracker: Advancing Myocardial Point Tracking in Echocardiography

arXiv: https://arxiv.org/abs/2405.08587 · HTML: https://mlsanity.com/paper/2405.08587/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 14 May 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [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)
- [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)
- [Tracking Everything Everywhere All at Once](https://mlsanity.com/paper/2306.05422/) (2023)
- [Multi-label Video Classification for Underwater Ship Inspection](https://mlsanity.com/paper/2305.17338/) (2023)

## Built on by (most cited first, 11 of 11)

- [Low Complexity Point Tracking of the Myocardium in 2D Echocardiography](https://mlsanity.com/paper/2503.10431/) (2025)
- [Taming Modern Point Tracking for Speckle Tracking Echocardiography via Impartial Motion](https://mlsanity.com/paper/2507.10127/) (2025)
- [Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning](https://mlsanity.com/paper/2511.02210/) (2025)
- [Anatomically Constrained Transformers for Cardiac Amyloidosis Classification](https://mlsanity.com/paper/2509.19691/) (2025)
- [Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?](https://mlsanity.com/paper/2605.28697/) (2026)
- [DUSTrack: Semi-automated point tracking in ultrasound videos](https://mlsanity.com/paper/2507.14368/) (2025)
- [Think as Cardiac Sonographers: Marrying SAM with Left Ventricular Indicators Measurements According to Clinical Guidelines](https://mlsanity.com/paper/2508.08566/) (2025)
- [Anatomically Constrained Transformers for Echocardiogram Analysis](https://mlsanity.com/paper/2511.01109/) (2025)
- [Point Tracking as a Temporal Cue for Robust Myocardial Segmentation in Echocardiography Videos](https://mlsanity.com/paper/2601.09207/) (2026)
- [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)

Tissue tracking in echocardiography is challenging due to the complex cardiac motion and the inherent nature of ultrasound acquisitions. Although optical flow methods are considered state-of-the-art (SOTA), they struggle with long-range tracking, noise occlusions, and drift throughout the cardiac cycle. Recently, novel learning-based point tracking techniques have been introduced to tackle some of these issues. In this paper, we build upon these techniques and introduce EchoTracker, a two-fold coarse-to-fine model that facilitates the tracking of queried points on a tissue surface across ultrasound image sequences. The architecture contains a preliminary coarse initialization of the trajectories, followed by reinforcement iterations based on fine-grained appearance changes. It is efficient, light, and can run on mid-range GPUs. Experiments demonstrate that the model outperforms SOTA methods, with an average position accuracy of 67% and a median trajectory error of 2.86 pixels. Furthermore, we show a relative improvement of 25% when using our model to calculate the global longitudinal strain (GLS) in a clinical test-retest dataset compared to other methods. This implies that learning-based point tracking can potentially improve performance and yield a higher diagnostic and prognostic value for clinical measurements than current techniques. Our source code is available at: https://github.com/riponazad/echotracker/.