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 $\pm$ 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.

翻译：用于点追踪的深度学习方法适用于二维超声心动图，但尚未充分利用领域特性以实现极快且高效的配置。我们开发了MyoTracker，一种用于超声心动图点追踪的低复杂度架构（0.3M参数）。它以CoTracker2架构为基础，通过简化其组件并扩展时间上下文，以单步方式为整个序列提供点预测。我们将MyoTracker应用于右心室（RV）聚焦记录中的右心室心肌，并将结果与CoTracker2以及另一种专用于超声心动图的点追踪架构EchoTracker进行了比较。MyoTracker实现了最低的平均点轨迹误差，为2.00 $\pm$ 0.53 mm。使用MyoTracker的点预测计算右心室游离壁应变（RV FWS），与商业软件的参考值相比，偏差为-0.3$\%$，95$\%$一致性界限为-6.1$\%$至5.4$\%$。该范围落在先前研究报告的观察者间变异范围内。CoTracker2和EchoTracker的一致性界限均更宽，差于观察者间变异。在推理时，对于长序列（100帧），MyoTracker使用的GPU内存比CoTracker2少67$\%$，比EchoTracker少84$\%$。在我们的设置下，MyoTracker的推理速度比CoTracker2快74倍，比EchoTracker快11倍。在时间上下文中保持整个序列是MyoTracker准确性的最主要贡献因素。通过重新启用迭代细化可以获得轻微的额外增益，但代价是更长的处理时间。