ArticleInsights into imaging2020
Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI.
Article in Insights into imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Quantifying User Satisfaction: Weighted Metric Approach for Evaluating Deep Learning-Based Thigh MRI Segmentations.Diagnostics (Basel, Switzerland) · 2026Article
- Hepatic Fat Quantification Using Beta Distribution and a Probabilistic Neural Network in a Prepubertal Male Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based framework for comprehensive quantification of thigh and calf muscles and adipose tissues from MRI.Magma (New York, N.Y.) · 2026Article
- Reference Methods for Measuring Skeletal Muscle Mass: A Critical Perspective.Journal of cachexia, sarcopenia and muscle · 2026Review
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- Automated analysis of quantitative muscle MRI and its reliability in patients with Duchenne muscular dystrophy.Journal of neuromuscular diseases · 2025Article
- Gender difference in cross-sectional area and fat infiltration of thigh muscles in the elderly population on MRI: an AI-based analysis.European radiology experimental · 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
- Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.Magma (New York, N.Y.) · 2025Article
- Muscle fat infiltration: a narrative review of the magnetic resonance (MR)-based evaluation methods and their clinical applications.Quantitative imaging in medicine and surgery · 2024Review
- 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
- Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients.Journal of functional morphology and kinesiology · 2024Article
- Human lower leg muscles grow asynchronously.Journal of anatomy · 2024Article
- Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.PloS one · 2024Article
- Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.PloS one · 2024Article
- Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.Nuklearmedizin. Nuclear medicine · 2023Review
- Semi-automatic muscle segmentation in MR images using deep registration-based label propagation.Pattern recognition · 2023Article
- Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.Magnetic resonance in medicine · 2023Article
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Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTime-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat-water decomposition MRI.
resultsThis study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs.
conclusionsThis automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.
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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.