ArticleMagma (New York, N.Y.)2020
Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Magnetic Resonance Imaging Segmentation of Soft Tissue in the Diagnosis of Chronic Low Back Pain: A Scoping Review.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.Journal of cachexia, sarcopenia and muscle · 2024Article
- Magnetic Resonance Imaging Biomarkers of Muscle.Tomography (Ann Arbor, Mich.) · 2024Review
- Mid-life cyclists preserve muscle mass and composition: a 3D MRI study.BMC musculoskeletal disorders · 2023Article
- Gender similarities and differences in skeletal muscle and body composition: an MRI study of recreational cyclists.BMJ open sport & exercise medicine · 2023Article
- Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: a scoping review of lateral hip musculature.BMC musculoskeletal disorders · 2022Article
- Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance.Applied bionics and biomechanics · 2022Article
- Intramuscular fat in gluteus maximus for different levels of physical activity.Scientific reports · 2021Article
- Overview of MR Image Segmentation Strategies in Neuromuscular Disorders.Frontiers in neurology · 2021Review
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Authors and funding
5 authors.
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Abstract
objectiveTo design, develop and evaluate an automated multi-atlas method for segmentation and volume quantification of gluteus maximus from Dixon and T1-weighted images. MATERIALS AND
methodsThe multi-atlas segmentation method uses an atlas library constructed from 15 Dixon MRI scans of healthy subjects. A non-rigid registration between each atlas and the target, followed by majority voting label fusion, is used in the segmentation. We propose a region of interest (ROI) to standardize the measurement of muscle bulk. The method was evaluated using the dice similarity coefficient (DSC) and the relative volume difference (RVD) as metrics, for Dixon and T1-weighted target images.
resultsThe mean(± SD) DSC was 0.94 ± 0.01 for Dixon images, while 0.93 ± 0.02 for T1-weighted. The RVD between the automated and manual segmentation had a mean(± SD) value of 1.5 ± 4.3% for Dixon and 1.5 ± 4.8% for T1-weighted images. In the muscle bulk ROI, the DSC was 0.95 ± 0.01 and the RVD was 0.6 ± 3.8%.
conclusionThe method allows an accurate fully automated segmentation of gluteus maximus for Dixon and T1-weighted images and provides a relatively accurate volume measurement in shorter times (~ 20 min) than the current gold-standard manual segmentations (2 h). Visual inspection of the segmentation would be required when higher accuracy is needed.
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