ArticleInternational journal of computer assisted radiology and surgery2020
Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 70 citations in OpenAlex.
- Article
- Pose-aware deep perceptual similarity for iterative 2D/3D registration of knee joints using contrastive learning.International journal of computer assisted radiology and surgery · 2026Article
- Neural Implicit Shape and Intensity Models for Scan-Free 2D-3D Registration in Dynamic Stereo-Radiography.Annals of biomedical engineering · 2026Article
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations.Advances in neural information processing systems · 2025Article
- End-to-end 2D/3D registration from pre-operative MRI to intra-operative fluoroscopy for orthopedic procedures.International journal of computer assisted radiology and surgery · 2025Article
- Article
- Single-View Fluoroscopic X-Ray Pose Estimation: A Comparison of Alternative Loss Functions and Volumetric Scene Representations.Journal of imaging informatics in medicine · 2025Article
- Automatic multi-view X-ray/CT registration using bone substructure contours.International journal of computer assisted radiology and surgery · 2025Article
- Uncertainty Quantification in Image-based 2D/3D Registration and Its Relationship with Accuracy.International journal of computer assisted radiology and surgery · 2025Article
- Intelligent control of robotic X-ray devices using a language-promptable digital twin.International journal of computer assisted radiology and surgery · 2025Article
- Autonomous Robotic Systems in Otolaryngology-Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Take a shot! Natural language control of intelligent robotic X-ray systems in surgery.International journal of computer assisted radiology and surgery · 2024Article
- Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- 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
- A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers.IEEE transactions on medical imaging · 2024Article
- Colonoscopy 3D video dataset with paired depth from 2D-3D registration.Medical image analysis · 2023Article
- Efficacy and reliability of three-dimensional fusion guidance for fluoroscopic navigation in transarterial embolization for refractory musculoskeletal pain.Quantitative imaging in medicine and surgery · 2023Article
- An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation.International journal of computer assisted radiology and surgery · 2023Article
- LatentPCN: latent space-constrained point cloud network for reconstruction of 3D patient-specific bone surface models from calibrated biplanar X-ray images.International journal of computer assisted radiology and surgery · 2023Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeFluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the surgical workflow and diminishes any advantages gained from navigation. In this paper, we propose a method for fully automatic registration using anatomical annotations produced by a neural network.
methodsNeural networks are trained to simultaneously segment anatomy and identify landmarks in fluoroscopy. Training data are obtained using a computationally intensive, intraoperatively incompatible, 2D/3D registration of the pelvis and each femur. Ground truth 2D segmentation labels and anatomical landmark locations are established using projected 3D annotations. Intraoperative registration couples a traditional intensity-based strategy with annotations inferred by the network and requires no human assistance.
resultsGround truth segmentation labels and anatomical landmarks were obtained in 366 fluoroscopic images across 6 cadaveric specimens. In a leave-one-subject-out experiment, networks trained on these data obtained mean dice coefficients for left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84, respectively. The mean 2D landmark localization error was 5.0 mm. The pelvis was registered within [Formula: see text] for 86% of the images when using the proposed intraoperative approach with an average runtime of 7 s. In comparison, an intensity-only approach without manual initialization registered the pelvis to [Formula: see text] in 18% of images.
conclusionsWe have created the first accurately annotated, non-synthetic, dataset of hip fluoroscopy. By using these annotations as training data for neural networks, state-of-the-art performance in fluoroscopic segmentation and landmark localization was achieved. Integrating these annotations allows for a robust, fully automatic, and efficient intraoperative registration during fluoroscopic navigation of the hip.
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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.