A deep learning pipeline for identification of motor units in musculoskeletal ultrasound

TL;DR

This paper introduces a deep learning pipeline that identifies active motor units in ultrasound image sequences, segmenting their territories and estimating their mechanical responses, enabling detailed analysis of muscle activity.

Contribution

It presents a novel deep learning approach combining 3D CNNs and neural networks for MU identification and signal estimation from ultrasound data, improving analysis of muscle activation.

Findings

Effective MU identification and segmentation demonstrated on simulated data.

Accurate estimation of twitch train signals at low contraction forces.

Framework maintains spatio-temporal information despite data transformation.

Abstract

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.