ArticlePain medicine (Malden, Mass.)2023
Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI.
Article in Pain medicine (Malden, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spine muscle auto segmentation techniques in MRI imaging: a systematic review.BMC musculoskeletal disorders · 2024Pooled it
- Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- Baseline characteristics of participants in the Biomarkers for Evaluating Spine Treatments clinical trial: a sequential multiple assignment randomized trial for chronic low back pain†.Pain medicine (Malden, Mass.) · 2025Trial
- Human In Vivo Disc Mechanics in Flexion, Extension, and Diurnal Loading With Aging and Degeneration.JOR spine · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.NPJ digital medicine · 2026Article
- Artificial Intelligence-Based Proximal Bone Shape Asymmetry Analysis and Clinical Correlation with Cartilage Relaxation Times and Functional Activity.Bioengineering (Basel, Switzerland) · 2026Article
- Advances in mechanical assessments of in vivo human lumbar spine tissues with noninvasive imaging techniques.npj biomedical innovations · 2026Review
- Mechanotransduction in intervertebral disc degeneration: from compartment-specific sensors to translational frontiers.Frontiers in bioengineering and biotechnology · 2026Review
- Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images.Scientific reports · 2025Article
- Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 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
- Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.JOR spine · 2024Article
- Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level.JOR spine · 2024Article
- Article
- Accurate Intervertebral Disc Segmentation Approach Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.Pain medicine (Malden, Mass.) · 2023Article
- MRI-based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading.JOR spine · 2023Article
- K2S Challenge: From Undersampled K-Space to Automatic Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
STUDY
designIn vivo retrospective study of fully automatic quantitative imaging feature extraction from clinically acquired lumbar spine magnetic resonance imaging (MRI).
objectiveTo demonstrate the feasibility of substituting automatic for human-demarcated segmentation of major anatomic structures in clinical lumbar spine MRI to generate quantitative image-based features and biomechanical models.
settingPrevious studies have demonstrated the viability of automatic segmentation applied to medical images; however, the feasibility of these networks to segment clinically acquired images has not yet been demonstrated, as they largely rely on specialized sequences or strict quality of imaging data to achieve good performance.
methodsConvolutional neural networks were trained to demarcate vertebral bodies, intervertebral disc, and paraspinous muscles from sagittal and axial T1-weighted MRIs. Intervertebral disc height, muscle cross-sectional area, and subject-specific musculoskeletal models of tissue loading in the lumbar spine were then computed from these segmentations and compared against those computed from human-demarcated masks.
resultsSegmentation masks, as well as the morphological metrics and biomechanical models computed from those masks, were highly similar between human- and computer-generated methods. Segmentations were similar, with Dice similarity coefficients of 0.77 or greater across networks, and morphological metrics and biomechanical models were similar, with Pearson R correlation coefficients of 0.69 or greater when significant.
conclusionsThis study demonstrates the feasibility of substituting computer-generated for human-generated segmentations of major anatomic structures in lumbar spine MRI to compute quantitative image-based morphological metrics and subject-specific musculoskeletal models of tissue loading quickly, efficiently, and at scale without interrupting routine clinical care.
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Registered trials
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