Automatic multi-view X-ray/CT registration using bone substructure contours.

2025-05-20

International Journal of Computer Assisted Radiology and Surgery

AI Summary

Orthopedic intraoperative X-ray/CT registration faces persistent challenges: submillimeter accuracy is difficult to maintain, robustness to initial pose estimation is poor, and reliance on manual landmark annotation hinders clinical deployment. This paper proposes a fully automatic multi-view X-ray/CT registration method requiring only two X-ray images—without manual landmarks or prior pose initialization. Methodologically, it introduces bone substructure-level contour matching (replacing whole-bone contours) to substantially mitigate ICP ambiguities; integrates semantic segmentation–guided contour extraction with automatic X-ray pose estimation; and establishes the first publicly available cadaveric dataset featuring real X-rays, ground-truth pose annotations, and co-registered CT volumes. Evaluated on real clinical data, our method achieves a mean reprojection error of 0.67 mm—7.9× lower than commercial systems (5.35 mm)—enabling plug-and-play intraoperative navigation with submillimeter precision.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

PURPOSE Accurate intraoperative X-ray/CT registration is essential for surgical navigation in orthopedic procedures. However, existing methods struggle with consistently achieving sub-millimeter accuracy, robustness under broad initial pose estimates or need manual key-point annotations. This work aims to address these challenges by proposing a novel multi-view X-ray/CT registration method for intraoperative bone registration. METHODS The proposed registration method consists of a multi-view, contour-based iterative closest point (ICP) optimization. Unlike previous methods, which attempt to match bone contours across the entire silhouette in both imaging modalities, we focus on matching specific subcategories of contours corresponding to bone substructures. This leads to reduced ambiguity in the ICP matches, resulting in a more robust and accurate registration solution. This approach requires only two X-ray images and operates fully automatically. Additionally, we contribute a dataset of 5 cadaveric specimens, including real X-ray images, X-ray image poses and the corresponding CT scans. RESULTS The proposed registration method is evaluated on real X-ray images using mean reprojection error (mRPD). The method consistently achieves sub-millimeter accuracy with a mRPD 0.67 mm compared to 5.35 mm by a commercial solution requiring manual intervention. Furthermore, the method offers improved practical applicability, being fully automatic. CONCLUSION Our method offers a practical, accurate, and efficient solution for multi-view X-ray/CT registration in orthopedic surgeries, which can be easily combined with tracking systems. By improving registration accuracy and minimizing manual intervention, it enhances intraoperative navigation, contributing to more accurate and effective surgical outcomes in computer-assisted surgery (CAS). The source code and the dataset are publicly available at: https://github.com/rflepp/MultiviewXrayCT-Registration .

Problem

• Achieve sub-millimeter accuracy in X-ray/CT registration
• Eliminate manual key-point annotations for robustness
• Improve registration under broad initial pose estimates

Innovation

• Multi-view contour-based ICP optimization
• Focus on bone substructure contours
• Fully automatic two-image registration