ArticleJournal of medical imaging (Bellingham, Wash.)2019
Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Persistent Human Quadriceps Muscle Volume Deficits Following ACL Reconstruction Are Associated With No Detectable Between-Leg Differences in Vastus Lateralis Muscle Signaling at Rest or Immediately Following Resistance Exercise.Scandinavian journal of medicine & science in sports · 2026Article
- A generalisation study in deep learning-based segmentation of lower-limb muscles across different populations.Scientific reports · 2026Article
- Impact of Prior Hamstring Strain Injury on Muscle Morphology and Sprinting Biomechanics in Collegiate American Football Athletes.Scandinavian journal of medicine & science in sports · 2026Observational
- Changes in Muscle Volume Asymmetries Among Lower Extremity Muscles From Baseline Through 12 Months After Anterior Cruciate Ligament Reconstruction With Quadriceps Tendon Autograft.Orthopaedic journal of sports medicine · 2026Article
- Electrical impedance myography captures features of muscle structure measured by MRI and transcriptomic analysis in facioscapulohumeral muscular dystrophy.Journal of neuromuscular diseases · 2026Article
- Lower-Body Muscle Volumes Can Explain Half of the Variance in Sprint Speed Between Collegiate American Football Players.Scandinavian journal of medicine & science in sports · 2026Article
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.Scientific reports · 2025Article
- Multi-scale machine learning model predicts muscle and functional disease progression.Scientific reports · 2025Article
- Altered Metabolic Activity and Morphology of Lower Limb Muscles 1-2 Years Following Anterior Cruciate Ligament Reconstruction.Scandinavian journal of medicine & science in sports · 2025Article
- Childhood muscle growth: Reference curves for lower leg muscle volumes and their clinical application in cerebral palsy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- 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
- Hamstrings Volumetric Adaptations to a Four-Week Nordic Hamstring Exercise Protocol in Individuals with ACL Reconstruction: A Preliminary Case Series.International journal of sports physical therapy · 2025Article
- 3D finite element models reveal regional fatty infiltration modulates tibialis anterior force generating capacity in FSHD.PloS one · 2025Article
- Lower Extremity Muscle Volume in Unilateral and Bilateral Patellofemoral Pain: A Cross-Sectional Exploratory Study Including Superficial and Deep Muscles.Journal of athletic training · 2024Article
- Compositional and Functional MRI of Skeletal Muscle: A Review.Journal of magnetic resonance imaging : JMRI · 2024Review
- Efficient musculoskeletal annotation using free-form deformation.Scientific reports · 2024Article
- AI driven analysis of MRI to measure health and disease progression in FSHD.Scientific reports · 2024Article
- NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.International journal of radiation oncology, biology, physics · 2024Article
- Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression.Human molecular genetics · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-resolution magnetic resonance imaging with fat suppression can obtain accurate anatomical information of all 35 lower limb muscles and individual segmentation can facilitate quantitative analysis. However, due to limited contrast and edge information, automatic segmentation of the muscles is very challenging, especially for athletes whose muscles are all well developed and more compact than the average population. Deep convolutional neural network (DCNN)-based segmentation methods showed great promise in many clinical applications, however, a direct adoption of DCNN to lower limb muscle segmentation is challenged by the large three-dimensional (3-D) image size and lack of the direct usage of muscle location information. We developed a cascaded 3-D DCNN model with the first step to localize each muscle using low-resolution images and the second step to segment it using cropped high-resolution images with individually trained networks. The workflow was optimized to account for different characteristics of each muscle for improved accuracy and reduced training and testing time. A testing augmentation technique was proposed to smooth the segmentation contours. The segmentation performance of 14 muscles was within interobserver variability and 21 were slightly worse than humans.
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