ArticleJournal of cachexia, sarcopenia and muscle2025
Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.
Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Biomolecular condensates as dynamic regulators of musculoskeletal homeostasis, disease, and therapeutic challenges.Bone research · 2026Review
- Deep Learning for Analysis of Bone Marrow Adiposity: Breakthroughs from Recent Large-Scale Analyses in the UK Biobank.Current osteoporosis reports · 2026Review
- Artificial Intelligence-Based Proximal Bone Shape Asymmetry Analysis and Clinical Correlation with Cartilage Relaxation Times and Functional Activity.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Osteosarcopenia: key molecular mechanisms and translational perspectives.Frontiers in physiology · 2025Review
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9 authors.
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Abstract
backgroundAge-related conditions, such as osteoporosis and sarcopenia, alongside chronic diseases, can result in significant musculoskeletal tissue loss. This impacts individuals' quality of life and increases risk of falls and fractures. Computed tomography (CT) has been widely used for assessing musculoskeletal tissues. Although automatic techniques have been investigated for segmenting tissues in the abdomen and mid-thigh regions, studies in proximal hip remain limited. This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip.
methodsWe examined 300 participants (men, 73 ± 6 years) from two cohorts of the Osteoporotic Fractures in Men Study (MrOS). We manually segmented cortical bone, trabecular bone, marrow adipose tissue (MAT), haematopoietic bone marrow (HBM), muscle, intermuscular adipose tissue (IMAT) and subcutaneous adipose tissue (SAT) from CT scan images at the proximal hip level. Using these data, we trained a U-Net-like deep learning model for automatic segmentation. The association between model-generated quantitative results and outcome variables such as grip strength, chair sit-to-stand time, walking speed, femoral neck and spine bone mineral density (BMD), and total lean mass was calculated.
resultsAn average Dice similarity coefficient (DSC) above 90% was observed across all tissue types in the test dataset. Grip strength showed positive correlations with cortical bone area (coefficient: 0.95, 95% confidence interval: [0.10, 1.80]), muscle area (0.41, [0.19, 0.64]) and average Hounsfield unit for muscle adjusted for height squared (AHU/h
conclusionOur deep learning-based technique offers a fast and accurate method for segmentation and quantification of musculoskeletal tissues in proximal hip, with potential clinical value.
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