Leitner Christoph, Jarolim Robert, Konrad Andreas, Kruse Annika, Tilp Markus, Schrottner Jorg, Baumgartner Christian
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4770-4774. doi: 10.1109/EMBC44109.2020.9176145.
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 ± 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.
记录运动过程中肌肉肌腱连接处的位移，能够分别对肌肉和肌腱的行为进行独立研究。为了提供一种全自动跟踪方法，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处的位置。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。我们的数据集包括79名健康受试者和28名有运动限制的受试者，他们进行了被动全范围运动和最大收缩运动。我们训练的网络在质量各异的多样数据集上对肌肉肌腱连接处进行了稳健检测，平均绝对误差为2.55±1毫米。我们表明，我们的方法可应用于各种受试者，并且可以实时操作。完整的软件包可供开源使用。
