EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion

2026-05-12

AI Summary

This study addresses the limitations of existing echocardiographic myocardial point tracking methods, which often neglect the local spatiotemporal continuity of myocardial motion and struggle to accurately model its physiologically constrained deformation. To overcome this, the authors propose EchoTracker2, a novel approach that adopts a purely refinement-stage architecture—eliminating the coarse initialization step common in traditional coarse-to-fine pipelines—to better align with the locally restricted nature of myocardial motion. EchoTracker2 integrates pixel-level features, local spatiotemporal context modeling, and long-range temporal reasoning, while explicitly incorporating myocardial motion constraints. Experimental results demonstrate that EchoTracker2 outperforms state-of-the-art models, achieving a 6.5% improvement in positional accuracy, a 12.2% reduction in median trajectory error, higher agreement with expert-derived global longitudinal strain (GLS), and significantly enhanced test–retest reproducibility.

Abstract

Myocardial point tracking (MPT) has recently emerged as a promising direction for motion estimation in echocardiography, driven by advances in general-purpose point tracking methods. However, myocardial motion fundamentally differs from motion encountered in natural videos, as it arises from physiologically constrained deformation that is spatially and temporally continuous throughout the cardiac cycle. Consequently, motion trajectories typically remain locally confined despite substantial tissue deformation. Motivated by these properties, we revisit the architectural design for MPT and find that coarse initialization in commonly used two-stage coarse-to-fine architectures may be unnecessary in this domain. In this work, we propose a fine-stage-only architecture, \textbf{EchoTracker2}, which enriches pixel-precise features with local spatiotemporal context and integrates them with long-range joint temporal reasoning for robust tracking. Experimental results across in-distribution, out-of-distribution (OOD), and public synthetic datasets show that our model improves position accuracy by $6.5\%$ and reduces median trajectory error by $12.2\%$ relative to a domain-specific state-of-the-art (SOTA) model. Compared to the best general-purpose point tracking method, the improvements are $2.0\%$ and $5.3\%$, respectively. Moreover, EchoTracker2 shows better agreement with expert-derived global longitudinal strain (GLS) and enhances test-rest reproducibility. Source code will be available at: https://github.com/riponazad/ptecho.

Problem

• Myocardial point tracking
• Echocardiography
• Motion estimation
• Cardiac motion
• Spatiotemporal continuity

Innovation

• myocardial point tracking
• local spatiotemporal context
• fine-stage-only architecture
• temporal reasoning
• echocardiography