ArticleSkeletal radiology2020
Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.
Article in Skeletal radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Association between low skeletal muscle mass assessed by deep learning-based CT and MASLD: a prospective cohort study.Diabetology & metabolic syndrome · 2026Article
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- 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
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Automated segmentation of the fibula from CT imaging using two-stepped deep learning in 3D U-Net architectures.Scientific reports · 2025Article
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education.IEEE transactions on medical robotics and bionics · 2025Article
- The Critical Role of Body Composition Assessment in Advancing Research and Clinical Health Risk Assessment across the Lifespan.Journal of obesity & metabolic syndrome · 2025Review
- Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Article
- Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.Scientific reports · 2025Article
- Automated segmentation of trunk musculature with a deep CNN trained from sparse annotations in radiation therapy patients with metastatic spine disease: an observational study.Frontiers in bioengineering and biotechnology · 2025Article
- The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.Scientific reports · 2024Article
- Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan.Research in diagnostic and interventional imaging · 2024Article
- Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample.Nutrients · 2024Article
- Accuracy and Reliability of Pelvimetry Measures Obtained by Manual or Automatic Labeling of Three-Dimensional Pelvic Models.Journal of clinical medicine · 2024Article
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.BMC bioinformatics · 2023Article
- A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images.European radiology experimental · 2023Article
- Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.Skeletal radiology · 2023Article
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5 authors.
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Abstract
objectiveTo develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. MATERIALS AND
methodsWe manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.
resultsThe CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).
conclusionsOur CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
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