ArticleScientific reports2021
2D-3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Recent Innovations and Advances in Japanese Hand Surgery.International orthopaedics · 2026Review
- 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
- AI-Assisted segmentation and volumetric reconstruction of radiographs through multi-angular scintillation imaging.Nature communications · 2026Article
- BIBSNet: A deep learning baby image brain segmentation network for MRI scans.Developmental cognitive neuroscience · 2026Article
- Real-time reconstruction of 3D bone models via very-low-dose protocols.NPJ digital medicine · 2026Article
- 3D path planning for robot-assisted vertebroplasty from arbitrary Bi-plane X-ray via differentiable rendering.Frontiers in robotics and AI · 2026Article
- Intraoperative 3D reconstruction from sparse arbitrarily posed real X-rays.Scientific reports · 2025Article
- Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.Annals of biomedical engineering · 2024Article
- Extra-Articular Distal Humerus Plate 3D Model Creation by Using the Method of Anatomical Features.Materials (Basel, Switzerland) · 2023Article
- Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images.Visual computing for industry, biomedicine, and art · 2023Review
- Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.Nature machine intelligence · 2023Article
- Iterative Online 3D Reconstruction from RGB Images.Sensors (Basel, Switzerland) · 2022Article
- [Rapid femur modeling method based on statistical shape model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data.Journal of imaging · 2022Article
- Smoothing the Undersampled Carpal Bone Model with Small Volume and Large Curvature: A Feasibility Study.Life (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.