ArticleSensors (Basel, Switzerland)2019
Paraspinal Muscle Segmentation Based on Deep Neural Network.
Article in Sensors (Basel, Switzerland), 2019. 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.
- Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-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 · 2023Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.Journal of advanced research · 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
- Technologies and Sensors for Artificial Muscles in Rehabilitation.Sensors (Basel, Switzerland) · 2024Review
- Accelerated muscle mass estimation from CT images through transfer learning.BMC medical imaging · 2024Article
- Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.JOR spine · 2024Article
- Assessment of lumbar paraspinal muscle morphology using mDixon Quant magnetic resonance imaging (MRI): a cross-sectional study in healthy subjects.Quantitative imaging in medicine and surgery · 2024Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- 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
- Reliability of panoramic ultrasound imaging and agreement with magnetic resonance imaging for the assessment of lumbar multifidus anatomical cross-sectional area.Scientific reports · 2023Article
- Comparison of multifidus degeneration between scoliosis and lumbar disc herniation.BMC musculoskeletal disorders · 2022Article
- Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults.Frontiers in rehabilitation sciences · 2022Article
- Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases.European radiology · 2021Article
- Different Approaches to Analyze Muscle Fat Replacement With Dixon MRI in Pompe Disease.Frontiers in neurology · 2021Article
- Research Progress on the Mechanism of Lumbarmultifidus Injury and Degeneration.Oxidative medicine and cellular longevity · 2021Review
- LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images.BMC musculoskeletal disorders · 2020Article
- A Review on the Use of Artificial Intelligence in Spinal Diseases.Asian spine journal · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate segmentation of the paraspinal muscle in Magnetic Resonance (MR) images is a critical step in the automated analysis of lumbar diseases such as chronic low back pain, disc herniation and lumbar spinal stenosis. However, the automatic segmentation of multifidus and erector spinae has not yet been achieved due to three unusual challenges: (1) the muscle boundary is unclear; (2) the gray histogram distribution of the target overlaps with the background; (3) the intra- and inter-patient shape is variable. We propose to tackle the problem of the automatic segmentation of paravertebral muscles using a deformed U-net consisting of two main modules: the residual module and the feature pyramid attention (FPA) module. The residual module can directly return the gradient while preserving the details of the image to make the model easier to train. The FPA module fuses different scales of context information and provides useful salient features for high-level feature maps. In this paper, 120 cases were used for experiments, which were provided and labeled by the spine surgery department of Shengjing Hospital of China Medical University. The experimental results show that the model can achieve higher predictive capability. The dice coefficient of the multifidus is as high as 0.949, and the Hausdorff distance is 4.62 mm. The dice coefficient of the erector spinae is 0.913 and the Hausdorff distance is 7.89 mm. The work of this paper will contribute to the development of an automatic measurement system for paraspinal muscles, which is of great significance for the treatment of spinal diseases.
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