ArticleScientific reports2021
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Spine muscle auto segmentation techniques in MRI imaging: a systematic review.BMC musculoskeletal disorders · 2024Pooled it
- Quantitative MRI changes in neck muscle size and composition following long-duration spaceflight.Experimental physiology · 2026Article
- A Magnetic Resonance Imaging (MRI) Investigation of Cervical Spine Muscle Characteristics After Concussion: Identifying Return-to-Play Clearance and Subsequent Injury Prognosis.Journal of child neurology · 2026Article
- Clinical significance and future applications of standardized imaging parameters for assessing paraspinal muscle fatty infiltration in lumbar degenerative disease: a narrative review.Journal of orthopaedic surgery and research · 2026Review
- Automated deep-learning quantification of intramuscular fat in lumbar spine muscles on Dixon MRI: validation and normative reference values from 173 healthy adults.BMC medical imaging · 2026Article
- Technologies to investigate the mechanisms of force-based manipulations: a narrative methods review.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis.Journal of shoulder and elbow surgery · 2025Article
- Heart volume on health checkup CT scans inversely correlates with pulse rate: data-driven analysis using deep-learning segmentation.Japanese journal of radiology · 2025Article
- Sarcopenia and the management of spinal disease in the elderly.GeroScience · 2025Review
- Leg Muscle Volume, Intramuscular Fat and Force Generation: Insights From a Computer-Vision Model and Fat-Water MRI.Journal of cachexia, sarcopenia and muscle · 2025Article
- Muscle fat infiltration: a narrative review of the magnetic resonance (MR)-based evaluation methods and their clinical applications.Quantitative imaging in medicine and surgery · 2024Review
- Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.Journal of cachexia, sarcopenia and muscle · 2024Article
- MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle.Journal of imaging · 2024Review
- Accelerated muscle mass estimation from CT images through transfer learning.BMC medical imaging · 2024Article
- Reduced Cervical Muscle Fat Infiltrate Is Associated with Self-Reported Recovery from Chronic Idiopathic Neck Pain Over Six Months: A Magnetic Resonance Imaging Longitudinal Cohort Study.Journal of clinical medicine · 2024Article
- Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study.The spine journal : official journal of the North American Spine Society · 2024Article
- Morphological Changes of Deep Extensor Neck Muscles in Relation to the Maximum Level of Cord Compression and Canal Compromise in Patients With Degenerative Cervical Myelopathy.Global spine journal · 2024Article
- Three-dimensional freehand ultrasonography to measure muscle volume of the lumbar multifidus: Reliability of processing technique and validity through comparison to magnetic resonance imaging.Journal of anatomy · 2024Article
- Article
- A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.PloS one · 2024Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
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.
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