ArticleScientific reports2024
Deep-learning-based pelvic automatic segmentation in pelvic fractures.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Automatic registration of generic bone models for X-ray-based biomechanics: Application to 3D rat hindlimb kinematics and sex-specific gait patterns.Computers in biology and medicine · 2026Article
- Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection.Scientific reports · 2026Article
- Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.Frontiers in artificial intelligence · 2026Review
- Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging.BMC musculoskeletal disorders · 2025Article
- An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians.Scientific reports · 2025Article
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4 authors.
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Abstract
With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the primary cause of death related to pelvic fractures. Considerable preliminary research has also been performed on segmenting tumors and organs. However, studies on clinically useful algorithms for bone and pelvic segmentation, based on developed models, are limited. In this study, we explored the potential of deep-learning models presented in previous studies to accurately segment pelvic regions in X-ray images. Data were collected from X-ray images of 940 patients aged 18 or older at Gachon University Gil Hospital from January 2015 to December 2022. To segment the pelvis, Attention U-Net, Swin U-Net, and U-Net were trained, thereby comparing and analyzing the results using five-fold cross-validation. The Swin U-Net model displayed relatively high performance compared to Attention U-Net and U-Net models, achieving an average sensitivity, specificity, accuracy, and dice similarity coefficient of 96.77%, of 98.50%, 98.03%, and 96.32%, respectively.
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