ArticleInsights into imaging2021
Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging.
Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Fusion of X-Ray Images and Clinical Data for a Multimodal Deep Learning Prediction Model of Osteoporosis: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- The application progress of artificial intelligence in osteoporosis diagnosis.Frontiers in artificial intelligence · 2025Review
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- An Automatic Method for Elbow Joint Recognition, Segmentation and Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- MAIC-10 brief quality checklist for publications using artificial intelligence and medical images.Insights into imaging · 2023Review
- Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundSegmentation of the ulna and radius is a crucial step for the measurement of bone mineral density (BMD) in dual-energy X-ray imaging in patients suspected of having osteoporosis. PURPOSE: This work aimed to propose a deep learning approach for the accurate automatic segmentation of the ulna and radius in dual-energy X-ray imaging. METHODS AND MATERIALS: We developed a deep learning model with residual block (Resblock) for the segmentation of the ulna and radius. Three hundred and sixty subjects were included in the study, and five-fold cross-validation was used to evaluate the performance of the proposed network. The Dice coefficient and Jaccard index were calculated to evaluate the results of segmentation in this study.
resultsThe proposed network model had a better segmentation performance than the previous deep learning-based methods with respect to the automatic segmentation of the ulna and radius. The evaluation results suggested that the average Dice coefficients of the ulna and radius were 0.9835 and 0.9874, with average Jaccard indexes of 0.9680 and 0.9751, respectively.
conclusionThe deep learning-based method developed in this study improved the segmentation performance of the ulna and radius in dual-energy X-ray imaging.
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Registered trials
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