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Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury
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....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538618/ https://www.ncbi.nlm.nih.gov/pubmed/31138878 http://dx.doi.org/10.1038/s41598-019-44416-8 |
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author | Weber, Kenneth A. Smith, Andrew C. Wasielewski, Marie Eghtesad, Kamran Upadhyayula, Pranav A. Wintermark, Max Hastie, Trevor J. Parrish, Todd B. Mackey, Sean Elliott, James M. |
author_facet | Weber, Kenneth A. Smith, Andrew C. Wasielewski, Marie Eghtesad, Kamran Upadhyayula, Pranav A. Wintermark, Max Hastie, Trevor J. Parrish, Todd B. Mackey, Sean Elliott, James M. |
author_sort | Weber, Kenneth A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6538618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65386182019-06-06 Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury Weber, Kenneth A. Smith, Andrew C. Wasielewski, Marie Eghtesad, Kamran Upadhyayula, Pranav A. Wintermark, Max Hastie, Trevor J. Parrish, Todd B. Mackey, Sean Elliott, James M. Sci Rep Article 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. Nature Publishing Group UK 2019-05-28 /pmc/articles/PMC6538618/ /pubmed/31138878 http://dx.doi.org/10.1038/s41598-019-44416-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Weber, Kenneth A. Smith, Andrew C. Wasielewski, Marie Eghtesad, Kamran Upadhyayula, Pranav A. Wintermark, Max Hastie, Trevor J. Parrish, Todd B. Mackey, Sean Elliott, James M. Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title | Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title_full | Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title_fullStr | Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title_full_unstemmed | Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title_short | Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury |
title_sort | deep learning convolutional neural networks for the automatic quantification of muscle fat infiltration following whiplash injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538618/ https://www.ncbi.nlm.nih.gov/pubmed/31138878 http://dx.doi.org/10.1038/s41598-019-44416-8 |
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