用于识别肌肉骨骼超声中运动单位的深度学习管道
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound
摘要：
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声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（抽搐训练）的信号估计。我们训练和评估模型使用模拟数据模仿复杂的激活模式的数十个激活MU重叠的领土和部分同步的激活模式。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，我们采用第二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，并估计他们的抽搐训练信号在低收缩力。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。
英文摘要：
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.
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