Patch-Based Image Similarity for Intraoperative 2D/3D Pelvis Registration During Periacetabular Osteotomy
Not on the map. Why?
Abstract (arXiv)
Periacetabular osteotomy is a challenging surgical procedure for treating developmental hip dysplasia, providing greater coverage of the femoral head via relocation of a patient's acetabulum. Since fluoroscopic imaging is frequently used in the surgical workflow, computer-assisted X-Ray navigation of osteotomes and the relocated acetabular fragment should be feasible. We use intensity-based 2D/3D registration to estimate the pelvis pose with respect to fluoroscopic images, recover relative poses of multiple views, and triangulate landmarks which may be used for navigation. Existing similarity metrics are unable to consistently account for the inherent mismatch between the preoperative intact pelvis, and the intraoperative reality of a fractured pelvis. To mitigate the effect of this mismatch, we continuously estimate the relevance of each pixel to solving the registration and use these values as weightings in a patch-based similarity metric. Limiting computation to randomly selected subsets of patches results in faster runtimes than existing patch-based methods. A simulation study was conducted with random fragment shapes, relocations, and fluoroscopic views, and the proposed method achieved a 1.7 mm mean triangulation error over all landmarks, compared to mean errors of 3 mm and 2.8 mm for the non-patched and image-intensity-variance-weighted patch similarity metrics, respectively.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 1.
9 papers.
- 2023Intraoperative 2D/3D Image Registration via Differentiable X-ray Renderingcited by 24
- 2024An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-EScited by 4
- 2024Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusioncited by 3
- 2025PolyPose: Deformable 2D/3D Registration via Polyrigid Transformationscited by 1
- 2025Human-AI Collaboration and Explainability for 2D/3D Registration Quality Assurancecited by 1
- 2025Intraoperative 2D/3D Registration via Spherical Similarity Learning and Differentiable Levenberg-Marquardt Optimizationcited by 1
- 2025Rapid patient-specific neural networks for intraoperative X-ray to volume registrationcited by 0
- 2025Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imagingcited by 0
- 2026Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registrationcited by 0
- 2026Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration25 Jul 2026
- 2025Intraoperative 2D/3D Registration via Spherical Similarity Learning and Differentiable Levenberg-Marquardt Optimization8 Sep 2025
- 2025Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging5 Sep 2025
- 2025Human-AI Collaboration and Explainability for 2D/3D Registration Quality Assurance23 Jul 2025
- 2025PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations25 May 2025
- 2025Rapid patient-specific neural networks for intraoperative X-ray to volume registration20 Mar 2025
- 2024An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES8 Feb 2024
- 2024Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion4 Feb 2024
- 2023Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering11 Dec 2023