A novel, fully automated multi-view X-ray/CT registration method for intraoperative usage offering sub-millimeter accuracy and strong robustness.
Pending patent application No. EP25168706.7.
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 multiview X-ray/CT registration method
for intraoperative non-convex bone registration.
Methods: The proposed registration method consists of a multiview, 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,
contour masks, X-ray image poses and the corresponding CT scans.
Results: The proposed registration method is evaluated on real X-ray images
using mean reprojection distance (mRPD). The method consistently achieves sub-millimeter accuracy with a
mRPD of 0.67mm compared to 5.35mm 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 multiview 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 IGS.
Two intraoperative X-ray images are acquired and used to extract the bone substructure contours. These contours are obtained using a semantic segmentation U-Net trained on patient-specific synthetic data. The loss function is specifically designed to favor thin, connected structures. In this example, we extract contours of the medial and lateral condyles as well as the femoral shaft. Simultaneously, the intrinsic and extrinsic parameters of the X-ray imaging system are calibrated using our custom fiducial marker (see supplementary material for details).
Preoperatively, the bone model is segmented from the CT scan, and its substructures are defined. Using the previously calibrated X-ray parameters, contours of the bone model and its respective substructures are projected onto the X-ray image planes.
The extracted bone substructure contours are aligned to the projected CT model contours using a multi-view Iterative Closest Point (ICP) optimization. The ICP optimization considers multiple views simultaneously, matching semantically extracted bone substructure contours with their corresponding projections from the CT model across the X-ray images. For simplicity, this example shows the process with only one view. The ICP optimization iteratively refines the alignment until convergence, yielding a robust and accurate registration solution by reducing ambiguity during matching.
This video demonstrates the complete registration process. On the left, you see the setup featuring two calibrated X-ray images. On the right, the iterative registration process is visualized, showing the alignment of CT model substructure reprojections with the extracted bone contours. The initial pose begins with a challenging 180° rotation around the femoral shaft axis, typically difficult for standard registration methods.
@article{Flepp2025,
author = {Flepp Roman and Nissen Leon and Sigrist Bastian and
Nieuwland Arend and Cavalcanti Nicola and
Fürnstahl Philipp and Dreher Thomas and Calvet Lilian},
title = {Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours},
journal = {International Journal of Computer Assisted Radiology and Surgery},
year = {2025},
month = may,
day = {20},
doi = {10.1007/s11548-025-03391-4},
url = {https://link.springer.com/article/10.1007/s11548-025-03391-4},
}