Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Journal: Scientific reports
PMID: 34400672

Abstract

Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to segment seven cervical spine muscle groups (left and right muscles segmented separately, 14 muscles total) from Dixon MRI scans (n = 17, 17 scans < 2 weeks post motor vehicle collision (MVC), and 17 scans 12 months post MVC). The CNN MFI measures demonstrated high test reliability and accuracy in an independent testing dataset (n = 18, 9 scans < 2 weeks post MVC, and 9 scans 12 months post MVC). Using the CNN in 84 participants with scans < 2 weeks post MVC (61 females, 23 males, age = 34.2 ± 10.7 years) differences in MFI between the muscle groups and relationships between MFI and sex, age, and body mass index (BMI) were explored. Averaging across all muscles, females had significantly higher MFI than males (p = 0.026). The deep cervical muscles demonstrated significantly greater MFI than the more superficial muscles (p < 0.001), and only MFI within the deep cervical muscles was moderately correlated to age (r > 0.300, p ≤ 0.001). CNN's allow for the accurate and rapid, quantitative assessment of the composition of the architecturally complex muscles traversing the cervical spine. Acknowledging the wider reports of MFI in cervical spine disorders and the time required to manually segment the individual muscles, this CNN may have diagnostic, prognostic, and predictive value in disorders of the cervical spine.

Authors

  • Kenneth A Weber
    Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.
  • Rebecca Abbott
    Evidence Synthesis Team, NIHR CLAHRC South West Peninsula (PenCLAHRC), College of Medicine and Health, University of Exeter, Exeter, UK.
  • Vivie Bojilov
    Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Andrew C Smith
    School of Physical Therapy, Regis University, Denver, CO, USA.
  • Marie Wasielewski
    Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Trevor J Hastie
    Stanford University, Stanford, CA, U.S.A.
  • Todd B Parrish
    Department of Radiology, Northwestern University, Chicago, IL.
  • Sean Mackey
    Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, USA.
  • James M Elliott
    Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

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