Abstract
Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated state-of-the-art performance in segmentation tasks. Here, we train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images from 39 participants (26 female, average = 31.7 ± 9.3 years) 3 months post whiplash injury. First, we demonstrate high test reliability and accuracy of the CNN compared to manual segmentation. Then we explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability. Across all participants, we demonstrate that CNN muscle volume was negatively correlated to pain (R = −0.415, p = 0.006) and disability (R = −0.286, p = 0.045), while CNN MFI tended to be positively correlated to disability (R = 0.214, p = 0.105). Additionally, CNN MFI was higher in participants with persisting pain and disability (p = 0.049). Overall, CNN’s may improve the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.
Original language | English (US) |
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Article number | 7973 |
Journal | Scientific reports |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2019 |
Funding
Research reported in this publication was supported by the National Institute of Child Health and Human Development/National Center for Medical Rehabilitation Research under award numbers R01HD079076 and R03HD094577, the National Institute of Drug Abuse under award numbers T32DA035165 and K24DA029262, and the National Institute of Neurological Disorders and Stroke under award number K23NS104211. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
ASJC Scopus subject areas
- General