EchoTracker: Advancing Myocardial Point Tracking in Echocardiography

TL;DR

EchoTracker is a novel learning-based method that significantly improves myocardial point tracking accuracy in echocardiography, enhancing clinical measurement reliability and potential diagnostic value.

Contribution

The paper introduces EchoTracker, a two-fold coarse-to-fine model that outperforms state-of-the-art methods in myocardial point tracking in ultrasound sequences.

Findings

Outperforms SOTA methods with 67% accuracy and 2.86 pixels median error.

Achieves 25% relative improvement in global longitudinal strain calculation.

Efficient and lightweight, suitable for mid-range GPUs.

Abstract

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/.