轻量级模型实现超快心肌点追踪，精度与效率双优。
Low Complexity Point Tracking of the Myocardium in 2D Echocardiography
- 基于CoTracker2简化结构，单步预测整序列点轨迹。
- 误差仅2.00±0.53mm，应变计算偏差小于1%，媲美人工测量。
- 推理速度比同类快74倍，显存占用减少超半，适合临床部署。
针对2D超声心动图中的点追踪任务，本文提出轻量级模型MyoTracker（仅0.3M参数），在简化CoTracker2结构的基础上扩展时间上下文，实现整序列单步预测。在右心室自由壁追踪中，其平均轨迹误差为2.00±0.53 mm，优于CoTracker2与EchoTracker。基于该模型计算的右心室自由壁应变（RV FWS）偏差仅为-0.3%，95%一致性界限为-6.1%至5.4%，在既往研究报道的人工差异范围内。而其他模型的界限更宽，超出人之间差异水平。推理时，MyoTracker比CoTracker2节省67%显存，比EchoTracker节省84%；速度分别提升74倍和11倍。保持完整序列时间上下文是精度关键，轻微改进可通过引入迭代优化实现，但会增加耗时。
原文摘要 · Abstract (English)
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.
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