ArticleMagma (New York, N.Y.)2020
Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain.
Article in Magma (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 51 citations in OpenAlex.
- Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.Physiological reports · 2025Trial
- Automated thigh muscle segmentation using foundation models: validation and impact of segmentation quality.European radiology experimental · 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
- An Analysis of the Efficacy of Deep Learning-Based Pectoralis Muscle Segmentation in Chest CT for Sarcopenia Diagnosis.Journal of imaging informatics in medicine · 2025Article
- Automated analysis of quantitative muscle MRI and its reliability in patients with Duchenne muscular dystrophy.Journal of neuromuscular diseases · 2025Article
- A Minimal Annotation Pipeline for Deep Learning Segmentation of Skeletal Muscles.NMR in biomedicine · 2025Article
- Semantic segmentation for individual thigh skeletal muscles of athletes on magnetic resonance images.Radiological physics and technology · 2025Article
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.PloS one · 2024Article
- Review
- Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.Magnetic resonance in medicine · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults.Frontiers in rehabilitation sciences · 2022Article
- Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.Journal of magnetic resonance imaging : JMRI · 2021Review
- Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.Magma (New York, N.Y.) · 2021Article
- MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate.IEEE access : practical innovations, open solutions · 2021Article
- Overview of MR Image Segmentation Strategies in Neuromuscular Disorders.Frontiers in neurology · 2021Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
objectiveSegmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. MATERIALS AND
methodsThe segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain.
resultsThe segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (- 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (- 5.6 ± 7.6%, p < 0.001, effect size: 0.73). DISCUSSION: Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.
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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.