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

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...

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Veröffentlicht in:	Scientific reports 2021-08, Vol.11 (1), p.16567-16567, Article 16567
Hauptverfasser:	Weber, Kenneth A., Abbott, Rebecca, Bojilov, Vivie, Smith, Andrew C., Wasielewski, Marie, Hastie, Trevor J., Parrish, Todd B., Mackey, Sean, Elliott, James M.
Format:	Artikel
Sprache:	eng
Schlagworte:	631/1647/245/1628 692/308/53 692/698/1671 Accuracy Age Artificial intelligence Automation Body mass index Datasets Deep learning Humanities and Social Sciences Infiltration Magnetic resonance imaging Medical imaging Medicine Motor vehicles multidisciplinary Muscles Neural networks Pain Pathology Science Science (multidisciplinary) Segmentation Spine Spine (cervical) Vehicles
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Beschreibung
Zusammenfassung:	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
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-021-95972-x