Biomedical Engineering Graduate Program, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
PLoS One. 2022 Mar 24;17(3):e0265752. doi: 10.1371/journal.pone.0265752. eCollection 2022.
Ultrasound imaging is valuable for non-invasively estimating fascicle lengths and other features of pennate muscle, especially when performed computationally. Effective analysis techniques to date typically use optic flow to track displacements from image sequences, but are sensitive to integration drift for longer sequences. We here present an alternative algorithm that objectively estimates geometric features of pennate muscle from ultrasound images, without drift sensitivity. The algorithm identifies aponeuroses and estimates fascicle angles to derive fascicle lengths. Length estimates of human vastus lateralis and gastrocnemius fascicles in healthy subjects (N = 9 and N = 17 respectively) compared well (overall root-mean-square difference, RMSD = 0.52 cm) to manual estimates by independent observers (n = 3), with overall coefficient of multiple correlation (CMC) of 0.98. Our tests yielded accuracy (CMC, RMSD) and processing speed similar to or exceeding that of state-of-the-art algorithms. The algorithm requires minimal manual intervention and can optionally extrapolate fascicle lengths that extend beyond the image frame. It thus facilitates automated analysis of ultrasound images without drift.
超声成像是一种非侵入性的方法，可用于无创估计羽状肌的肌束长度和其他特征，特别是在进行计算时。迄今为止，有效的分析技术通常使用光流来跟踪图像序列中的位移，但对于较长的序列，它们对积分漂移很敏感。我们在此提出了一种替代算法，该算法可以客观地从超声图像中估计羽状肌的几何特征，而不会受到漂移的影响。该算法可以识别腱膜并估计肌束角度，从而得出肌束长度。与独立观察者的手动估计相比（分别为 n = 3），健康受试者的股外侧肌和比目鱼肌肌束的长度估计（总体均方根差 RMSD = 0.52 cm）与手动估计吻合得很好（总体多重相关系数 CMC = 0.98）。我们的测试结果表明，该算法的准确性（CMC、RMSD）和处理速度与最先进的算法相似或超过。该算法需要最少的手动干预，并且可以选择推断超出图像帧的肌束长度。因此，它可以促进无漂移的超声图像自动分析。
