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: this https URL

本文引入了 EchoTracker，一种基于学习的点追踪技术，在超声图像序列中实现对组织表面上查询点的追踪，该技术能够提高超声心动图中的定位和测量精度，为临床诊断和预后评估提供更高的价值。