Foreground Detection Analysis of Ultrasound Image Sequences Identifies Markers of Motor Neurone Disease across Diagnostically Relevant Skeletal Muscles |
| |
Authors: | Kate Bibbings Peter J. Harding Ian D. Loram Nicholas Combes Emma F. Hodson-Tole |
| |
Affiliation: | 2. Crime and Well-Being Big Data Centre, Manchester Metropolitan University, Manchester, United Kingdom;3. Elements Technology Platforms Ltd., Cheshire, United Kingdom;4. Department of Neurophysiology, Preston Royal Hospital, Lancashire Teaching Hospital Trust, Preston, United Kingdom |
| |
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 |
本文献已被 ScienceDirect 等数据库收录! |
|