Foreground Detection Analysis of Ultrasound Image Sequences Identifies Markers of Motor Neurone Disease across Diagnostically Relevant Skeletal Muscles

Abstract

Diagnosis of motor neurone disease (MND) includes detection of small, involuntary muscle excitations, termed fasciculations. There is need to improve diagnosis and monitoring of MND through provision of objective markers of change. Fasciculations are visible in ultrasound image sequences. However, few approaches that objectively measure their occurrence have been proposed; their performance has been evaluated in only a few muscles; and their agreement with the clinical gold standard for fasciculation detection, intramuscular electromyography, has not been tested. We present a new application of adaptive foreground detection using a Gaussian mixture model (GMM), evaluating its accuracy across five skeletal muscles in healthy and MND-affected participants. The GMM provided good to excellent accuracy with the electromyography ground truth (80.17%-92.01%) and was robust to different ultrasound probe orientations. The GMM provides objective measurement of fasciculations in each of the body segments necessary for MND diagnosis and hence could provide a new, clinically relevant disease marker.

Keywords: Amyotrophic lateral sclerosis; Diagnostics; Electromyography; Feature tracking; Gaussian mixture model; Image processing; Myosonography; Neuromuscular; Ultrasonography.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Fig. 1

Examples of collected EMG signals without (a) and with (b and c) background voluntary activity. In (b) and (c), voluntary activity was removed using template matching, and the residual signal is shown in red. The solid horizontal line in (a)–(c) represents the threshold value used to identify fasciculation potentials. (d) Larger representation of the EMG signal in (b), with the firing instances of one unit denoted (red dots). Three representative examples of MUAP templates identified in the signal are provided (e), with template 1 (red) related to the firing instances in (d). The fasciculation potentials (f) exhibit more varied shapes than the MUAPs that, along with their sporadic occurrence, meant they were not identifiable using template matching approaches. EMG = electromyography; MUAP = motor unit action potential.

Fig. 2

Example Gaussian mixture models taken from intensity values of pixels in image sequences collected from Gastrocnemius medialis with the probe in longitudinal (a) and transverse (b) orientation. Each panel on the left contains data from a pixel located in a region influenced by a fasciculation event (operator confirmed). Panels on the right are from a pixel located in a region where a blood vessel was evident. Note how the mixture of Gaussians enables the different pixel intensity distributions to be described and how the lowest-weighted distributions occupy a different space compared with the higher-weighted ones for pixels in the region of a fasciculation.

Fig. 3

Receiver operating characteristic curves of Gaussian mixture model (blue) and Lucas–Kanade/mutual information (red) analysis approaches in Biceps brachii (BB, Left) and Gastrocnemius medialis (MG, Right) for different probe orientations (transverse: solid line, longitudinal: dashed line) in healthy (top) and patient (bottom) populations compared with the myoelectric truth signal. GMM = Gaussian mixture model; LK/MI = Lucas–Kanade/mutual information.

Fig. 4

Example results from LK/MI (top) and GMM (bottom) approaches (black lines) illustrating (a) good agreement between the two signals from Gastrocnemius medialis with easily detectable peaks in both signals (also see Supplementary Video S1, online only); (b, c) clear peaks in GMM but noisy and indistinct peaks from LK/MI approach resulting from breathing patterns affecting Trapezius (b, also see Supplementary Video S2, online only) and Rectus abdominis (c, also see Video 3). Number of foreground objects detected by the GMM has been normalized to one to facilitate display on the same scale as the EMG (grey lines) data, the scale of which is denoted by the axis on the right of each graph. EMG = electromyography; FG = foreground; GMM = Gaussian mixture model; LK/MI = Lucas–Kanade/mutual information.

Fig. 5

Receiver operating characteristic curves of Gaussian mixture model analysis approaches in Rectus abdominis (RA), thoracic paraspinals (TP) and Trapezius (TR) for different probe orientations (transverse: solid line, longitudinal: dashed line) in the patient population compared with the myoelectric truth signal.