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

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...

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Autores principales: Kenneth A. Weber, Rebecca Abbott, Vivie Bojilov, Andrew C. Smith, Marie Wasielewski, Trevor J. Hastie, Todd B. Parrish, Sean Mackey, James M. Elliott
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Publicado: Nature Portfolio 2021
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spelling oai:doaj.org-article:1c3ad2671064454da85d16c2d55f225a2021-12-02T16:46:35ZMulti-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions10.1038/s41598-021-95972-x2045-2322https://doaj.org/article/1c3ad2671064454da85d16c2d55f225a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95972-xhttps://doaj.org/toc/2045-2322Abstract 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.Kenneth A. WeberRebecca AbbottVivie BojilovAndrew C. SmithMarie WasielewskiTrevor J. HastieTodd B. ParrishSean MackeyJames M. ElliottNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Kenneth A. Weber
Rebecca Abbott
Vivie Bojilov
Andrew C. Smith
Marie Wasielewski
Trevor J. Hastie
Todd B. Parrish
Sean Mackey
James M. Elliott
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
description 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.
format article
author Kenneth A. Weber
Rebecca Abbott
Vivie Bojilov
Andrew C. Smith
Marie Wasielewski
Trevor J. Hastie
Todd B. Parrish
Sean Mackey
James M. Elliott
author_facet Kenneth A. Weber
Rebecca Abbott
Vivie Bojilov
Andrew C. Smith
Marie Wasielewski
Trevor J. Hastie
Todd B. Parrish
Sean Mackey
James M. Elliott
author_sort Kenneth A. Weber
title Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
title_short Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
title_full Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
title_fullStr Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
title_full_unstemmed Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
title_sort multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/1c3ad2671064454da85d16c2d55f225a
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