ArticleJournal of imaging2022
X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- X2BR: High-fidelity 3D bone reconstruction from a planar X-ray image with hybrid neural implicit methods.Medical & biological engineering & computing · 2026Article
- Artificial Intelligence in Intraoperative Imaging and Navigation for Spine Surgery: A Narrative Review.Journal of clinical medicine · 2026Review
- 3D spine reconstruction from a single radiograph based on GANs.Medical & biological engineering & computing · 2026Article
- 3D path planning for robot-assisted vertebroplasty from arbitrary Bi-plane X-ray via differentiable rendering.Frontiers in robotics and AI · 2026Article
- Automatic calibration of a multi-camera system with limited overlapping fields of view for 3D surgical scene reconstruction.International journal of computer assisted radiology and surgery · 2026Article
- Intraoperative 3D reconstruction from sparse arbitrarily posed real X-rays.Scientific reports · 2025Article
- Implementation of artificial intelligence (AI) in ASD treatment.North American Spine Society journal · 2025Article
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: an ex-vivo feasibility study.BMC musculoskeletal disorders · 2024Article
- Deep-learning based 3D reconstruction of lower limb bones from biplanar radiographs for preoperative osteotomy planning.International journal of computer assisted radiology and surgery · 2024Article
- Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review.Asian spine journal · 2024Article
- Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions.Journal of imaging · 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
- Translation of Medical AR Research into Clinical Practice.Journal of imaging · 2023Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients' lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow.
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Registered trials
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.