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

Kenneth A. Weber*, Rebecca Abbott, Vivie Bojilov, Andrew C. Smith, Marie Wasielewski, Trevor J. Hastie, Todd B. Parrish, Sean Mackey, James M. Elliott

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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.

Original languageEnglish (US)
Article number16567
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

Funding

This study was supported by grants from the National Institute of Child Health and Human Development/ National Center for Medical Rehabilitation Research (Grant Numbers R01HD079076, R03HD094577), the National Institute of Neurological Disorders and Stroke (Grant Numbers K23NS104211, L30NS108301), and the National Institute of Drug Abuse (Grant Number K24DA029262). 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

Fingerprint

Dive into the research topics of 'Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions'. Together they form a unique fingerprint.

Cite this