ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022
An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Automatability and validity of methods for the quantification of intra-/Intermuscular adipose tissue in conventional MRI: a systematic review.BMC medical imaging · 2025Pooled it
- Spine muscle auto segmentation techniques in MRI imaging: a systematic review.BMC musculoskeletal disorders · 2024Pooled it
- Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.Journal of advanced research · 2026Article
- Fat matters: paraspinal muscle quality is associated with electromyographic fatigability and endurance in lumbar spinal stenosis.North American Spine Society journal · 2026Article
- Article
- Deep learning-based assessment of paraspinal muscle degeneration and its relationships to muscle function and disability outcomes in chronic low back pain: a prospective study.European radiology · 2026Article
- Automated analysis of paraspinal muscles: segmentation and multi-parameter quantification in lumbar CT using convolutional neural network.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Artificial intelligence-driven assessment of sarcopenia in orthopedic geriatrics: technical progress and clinical implications.Frontiers in endocrinology · 2026Review
- Overlay of Nerve Roots to Aid in Augmented Reality-Guided L4 to L5 Transforaminal Interbody Fusion: A Methodologic Pilot.International journal of spine surgery · 2025Article
- Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI.Sensors (Basel, Switzerland) · 2025Article
- Clinical and Radiological Comparison of Unilateral Biportal Endoscopic and Percutaneous Transforaminal Endoscopic Discectomy in the Treatment of Lumbar Spinal Degenerative Disease.Orthopaedic surgery · 2025Article
- MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle.Journal of imaging · 2024Review
- Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.JOR spine · 2024Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- Fat beyond muscle: Assessing epimuscular fat of the lumbar spine and its association with vertebral level, demographics, BMI, and low back pain.Brain & spine · 2024Article
- Cross-sectional area and fat infiltration of the lumbar spine muscles in patients with back disorders: a deep learning-based big data analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Unilateral biportal endoscopy vs. open decompression for lumbar epidural lipomatosis-cohort study using a prospective registry.Frontiers in neurology · 2024Article
- DecompressionFrontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeImaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.
methodsA set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers.
resultsThe median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data.
conclusionsThe externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.