SynthesisBMC musculoskeletal disorders2024
Spine muscle auto segmentation techniques in MRI imaging: a systematic review.
Synthesis in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fatty infiltration and cross-sectional area as indicators of muscle mass in osteoporosis: a meta-regression study.Frontiers in endocrinology · 2025Pooled it
- Spinal Muscle Health in Chronic Low Back Pain: An Integrated Narrative Review of Muscle Quality, Function, and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- 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
- Frailty-Muscle Phenotypes Predict Outcomes After Lumbar Fusion in Adults Aged ≥75 Years: A Retrospective Cohort Study.Neurospine · 2026Article
- 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
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Automated segmentation of trunk musculature with a deep CNN trained from sparse annotations in radiation therapy patients with metastatic spine disease: an observational study.Frontiers in bioengineering and biotechnology · 2025Article
- Radiological frontiers in understanding paraspinal muscle pathophysiology in chronic low back pain.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe accurate segmentation of spine muscles plays a crucial role in analyzing musculoskeletal disorders and designing effective rehabilitation strategies. Various imaging techniques such as MRI have been utilized to acquire muscle images, but the segmentation process remains complex and challenging due to the inherent complexity and variability of muscle structures. In this systematic review, we investigate and evaluate methods for automatic segmentation of spinal muscles.
methodsData for this study were obtained from PubMed/MEDLINE databases, employing a search methodology that includes the terms 'Segmentation spine muscle' within the title, abstract, and keywords to ensure a comprehensive and systematic compilation of relevant studies. Systematic reviews were not included in the study.
resultsOut of 369 related studies, we focused on 12 specific studies. All studies focused on segmentation of spine muscle use MRI, in this systematic review subjects such as healthy volunteers, back pain patients, ASD patient were included. MRI imaging was performed on devices from several manufacturers, including Siemens, GE. The study included automatic segmentation using AI, segmentation using PDFF, and segmentation using ROI.
conclusionDespite advancements in spine muscle segmentation techniques, challenges still exist. The accuracy and precision of segmentation algorithms need to be improved to accurately delineate the different muscle structures in the spine. Robustness to variations in image quality, artifacts, and patient-specific characteristics is crucial for reliable segmentation results. Additionally, the availability of annotated datasets for training and validation purposes is essential for the development and evaluation of new segmentation algorithms. Future research should focus on addressing these challenges and developing more robust and accurate spine muscle segmentation techniques to enhance clinical assessment and treatment planning for musculoskeletal disorders.
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