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Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation

机译:基于相位分割的超声图像中的肌腱结自动跟踪

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摘要

Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic measurement method restricts its application in human motion analysis. This paper presents an automated measurement of MTJ displacement using prior knowledge on tendinous tissues and MTJ, precluding the influence of nontendinous components on the estimation of MTJ displacement. It is based on the perception of tendinous features from musculoskeletal ultrasound images using Radon transform and thresholding methods, with information about the symmetric measures obtained from phase congruency. The displacement of MTJ is achieved by tracking manually marked points on tendinous tissues with the Lucas-Kanade optical flow algorithm applied over the segmented MTJ region. The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from 10 healthy subjects (26.0 ± 2.9 years of age). Waveform similarity between the manual and automatic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlation (CMC). In vivo experiments demonstrated that MTJ tracking with the proposed method (CMC = 0.97 ± 0.02) was more consistent with the manual measurements than existing optical flow tracking methods (CMC = 0.79 ± 0.11). This study demonstrated that the proposed method was robust to the interference of nontendinous components, resulting in a more reliable measurement of MTJ displacement, which may facilitate further research and applications related to the architectural change of muscles and tendons.
机译:通过超声成像获得的肌腱接头(MTJ)的位移对于量化肌肉和肌腱的相互作用长度变化,对于理解运动过程中肌腱单元的力学和病理状况至关重要。然而,缺乏可靠的自动测量方法限制了其在人体运动分析中的应用。本文介绍了使用对腱组织和MTJ的先验知识进行的MTJ位移的自动测量,并排除了非腱成分对MTJ位移估计的影响。它基于使用Radon变换和阈值化方法从肌肉骨骼超声图像中获取肌腱特征的信息,以及有关从相位一致性获得的对称度量的信息。 MTJ的位移是通过使用在分段的MTJ区域上应用的Lucas-Kanade光流算法跟踪肌腱组织上的手动标记点来实现的。在从10位健康受试者(26.0±2.9岁)获得的腓肠肌超声图像上评估了该方法的性能。手动和自动测量之间的波形相似性是通过计算总体相似性与多重相关系数(CMC)来评估的。体内实验表明,与现有的光流跟踪方法(CMC = 0.79±0.11)相比,采用建议的方法(CMC = 0.97±0.02)进行MTJ跟踪与手动测量更加一致。这项研究表明,所提出的方法对于非弹性组件的干扰是鲁棒的,从而可以更可靠地测量MTJ位移,这可能有助于与肌肉和肌腱的结构变化有关的进一步研究和应用。

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