  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.
