A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture
A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture
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DOI：
10.1101/2022.04.20.488774
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发表时间：
2022-04
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影响因子：
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通讯作者：
Jasper Verheul;S. Yeo
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文献类型：
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作者：
Jasper Verheul;S. Yeo
利用B型超声跟踪骨骼肌结构是人体运动科学和生物力学领域广泛使用的一种方法。基于光流算法的顺序方法允许平滑和连贯的肌肉跟踪，但已知会随着时间的推移而漂移。另一方面，非顺序特征检测方法不受漂移的影响，但仅限于跟踪低维特征。众所周知，它们对图像噪声也很敏感，因此经常导致高度不规则的跟踪图案。在两种方法互补的基础上，我们提出了一种新的全自动混合式肌肉跟踪方法，该方法结合了序贯特征点跟踪方法和基于Hough变换的非序贯方法。在5个不同踝关节角度的等长收缩过程中，测量了5名健康人胫骨前束帆的角度、长度和中央腱膜的移位。与两种序贯方法相比，我们的混合方法显著减少了漂移(p<；0.001)，与非序贯方法相比，曲线不规则性显著减少(p<；0.001)。这些发现表明，所提出的混合方法可以独特地缓解用于跟踪骨骼肌结构的单个方法的漂移和不规则性限制。全自动肌肉跟踪允许方便地分析大数据集，而自动漂移校正为在常见运动期间(如行走、跑步和跳跃)的长超声记录中跟踪肌肉结构打开了大门，而不需要人工干预。
Tracking skeletal muscle architecture using B-mode ultra-sound is a widely used method in the field of human movement science and biomechanics. Sequential methods based on optical flow algorithms allow for smooth and coherent muscle tracking but are known to drift over time. Non-sequential feature detection methods on the other hand, do not suffer from drift, but are limited to tracking only lower-dimensional features. They are also known to be sensitive to image noise, and therefore often result in highly irregular tracking patterns. Building on the complimentary nature of both approaches, we present a novel fully automated hybrid muscle tracking approach that combines a sequential feature-point tracking method and a non-sequential method based on Hough transform. Tibialis anterior fascicle pennation angle and length, and central aponeurosis displacement, were measured in five healthy individuals during isometric contractions at five different ankle angles. Our hybrid method was demonstrated to significantly (p < 0.001) reduce drift compared to two sequential methods, and curve irregularity was significantly (p < 0.001) decreased compared to a non-sequential method. These findings suggest that the proposed hybrid approach can uniquely mitigate drift and irregularity limitation of individual methods used for tracking skeletal muscle architecture. Fully automated muscle tracking allows for convenient analysis of large datasets, whereas automatic drift correction opens the door for tracking muscle architecture in long ultrasound recordings during common movements, such as walking, running, and jumping without the need for manual intervention.