论文标题
使用深度学习在健康和受损受试者中自动跟踪肌肉肌腱连接
Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning
论文作者
论文摘要
在运动过程中记录肌肉肌腱连接位移，分别允许对肌肉和肌腱行为进行单独研究。为了提供一种完全自动的跟踪方法，我们采用了一种新颖的深度学习方法来检测超声图像中肌肉肌腱连接的位置。我们利用注意力机制使网络能够专注于相关区域并更好地解释结果。我们的数据集由大量的79名健康受试者和28名受试者组成，具有运动局限性，可以进行全面运动和最大收缩运动。我们训练有素的网络显示了对肌肉肌腱连接的强大检测，这些数据集的质量不同，平均绝对误差为2.55 $ \ pm $ 1毫米。我们证明我们的方法可以用于各种主题，并且可以实时操作。完整的软件包可供开放源包装使用：https：//github.com/luuleitner/deepmtj
Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ
