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

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.

Ultrasound-Based Detection of Fasciculations in Healthy and Diseased Muscles.

Involuntary muscle activations are diagnostic indicators of neurodegenerative pathologies. Currently detected by invasive intramuscular electromyography, these muscle twitches are found to be visible in ultrasound images. We present an automated computational approach for the detection of muscle twitches, and apply this to two muscles in healthy and motor neuron disease-affected populations. The technique relies on motion tracking within ultrasound sequences, extracting local movement information from muscle. A statistical analysis is applied to classify the movement, either as noise or as more coherent movement indicative of a muscle twitch. The technique is compared to operator identified twitches, which are also assessed to ensure operator agreement. We find that, when two independent operators manually identified twitches, higher interoperator agreement (Cohen's κ) occurs when more twitches are present (κ = 0.94), compared to a lower number (κ = 0.49). Finally, we demonstrate, via analysis of receiver operating characteristics, that our computational technique detects muscle twitches across the entire dataset with a high degree of accuracy (0.83 < accuracy < 0.96).