A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound
A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound
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DOI：
10.1109/access.2020.3023495
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Gronlund, Christer
中科院分区：
文献类型：
--
作者：
Ali, Hazrat;Umander, Johannes;Gronlund, Christer
骨骼肌在功能上由所谓的运动单位（MU）群体调节。MU包括由来自脊髓的神经元控制的一束肌肉纤维。目前诊断神经肌肉疾病和监测康复以及研究运动科学的方法依赖于记录和分析MU的生物电活动。然而，这些方法提供的信息来自肌肉的有限部分。超声成像提供了来自大部分肌肉的信息。最近已经表明，超快超声成像可以用于使用盲源分离来记录和分析单个MU的机械响应。在这项工作中，我们提出了一种替代方法-深度学习管道-来识别超声图像序列中的活动MU，包括对其领土的分割和对其机械响应（twitch train）的信号估计。我们使用模拟数据训练和评估模型，模拟数十个具有重叠区域和部分同步激活模式的激活MU的复杂激活模式。使用缓慢融合方法（基于3D CNN），我们将时空图像序列数据转换为2D表示，并应用深度神经网络架构进行分割。接下来，我们使用第二个深度神经网络架构进行信号估计。结果表明，建议的管道可以有效地识别单个MU，估计他们的领土，并估计他们的抽搐列车信号在低收缩力。即使当超声图像序列被变换成2D表示以与更传统的计算机视觉和图像处理技术兼容时，该框架也可以保留MU活动的机械响应的时空信息和信息。建议的管道是潜在的有用的，以确定同时活跃的MU在整个肌肉中的超声图像序列的自愿骨骼肌收缩在低力水平。
Skeletal muscles are functionally regulated by populations of so-called motor units (MUs). An MU comprises a bundle of muscle fibers controlled by a neuron from the spinal cord. Current methods to diagnose neuromuscular diseases and monitor rehabilitation, and study sports sciences rely on recording and analyzing the bio-electric activity of the MUs. However, these methods provide information from a limited part of a muscle. Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.