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Use of optical flow to estimate continuous changes in muscle thickness from ultrasound image sequences

机译:利用光流从超声图像序列估计肌肉厚度的连续变化

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摘要

Muscle thickness is one of the most widely used parameters for quantifying muscle function. Ultrasonography is frequently used to estimate changes in muscle thickness in both static and dynamic contractions. Conventionally, most such measurements are conducted by manual analysis of ultrasound images. This manual approach is time consuming, subjective, susceptible to error and not suitable for measuring dynamic change. In this study, we developed an automated tracking method based on an optical flow algorithm using an affine motion model. The goal of the study was to evaluate the performance of the proposed method by comparing it with the manual approach and by determining its repeatability. Real-time B-mode ultrasound was used to examine the rectus femoris during voluntary contraction. The coefficient of multiple correlation (CMC) was used to quantify the level of agreement between the two methods and the repeatability of the proposed method. The two methods were also compared by linear regression and Bland-Altman analysis. The findings indicated that the results obtained using the proposed method were in good agreement with those obtained using the manual approach (CMC=0.97±0.02, difference=-0.06±0.22mm) and were highly repeatable (CMC=0.91±0.07). In conclusion, the automated method proposed here provides an accurate, highly repeatable and efficient approach to the estimation of muscle thickness during muscle contraction.
机译:肌肉厚度是量化肌肉功能最广泛使用的参数之一。超声通常用于估计静态和动态收缩中肌肉厚度的变化。通常,大多数这样的测量是通过对超声图像进行手动分析来进行的。这种手动方法耗时,主观,容易出错并且不适合测量动态变化。在这项研究中,我们开发了一种基于仿射运动模型的光流算法的自动跟踪方法。该研究的目的是通过与手动方法进行比较并确定其可重复性来评估所提出方法的性能。实时B型超声用于检查自愿收缩过程中的股直肌。多重相关系数(CMC)用于量化两种方法之间的一致性水平以及所提出方法的可重复性。还通过线性回归和Bland-Altman分析比较了这两种方法。研究结果表明,使用所提出的方法获得的结果与使用手动方法获得的结果(CMC = 0.97±0.02,差异= -0.06±0.22mm)非常吻合,并且具有很高的可重复性(CMC = 0.91±0.07)。总之,这里提出的自动方法为估计肌肉收缩过程中的肌肉厚度提供了一种准确,高度可重复和有效的方法。

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