Pedicle Screw Pairing and Registration for Screw Pose Estimation from Dual C-arm Images Using CAD Models

Accurate matching of pedicle screws in both anteroposterior (AP) and lateral (LAT) images is critical for successful spinal decompression and stabilization during surgery. However, establishing screw correspondence, especially in LAT views, remains a significant clinical challenge. This paper introduces a method to address pedicle screw correspondence and pose estimation from dual C-arm images. By comparing screw combinations, the approach demonstrates consistent accuracy in both pairing and registration tasks. The method also employs 2D-3D alignment with screw CAD 3D models to accurately pair and estimate screw pose from dual views. Our results show that the correct screw combination consistently outperforms incorrect pairings across all test cases, even prior to registration. After registration, the correct combination further enhances alignment between projections and images, significantly reducing projection error. This approach shows promise for improving surgical outcomes in spinal procedures by providing reliable feedback on screw positioning.

Better Pose Initialization for Fast and Robust 2D/3D Pelvis Registration

This paper presents an approach for improving 2D/3D pelvis registration in optimization-based pose estimators using a learned initialization function. Current methods often fail to converge to the optimal solution when initialized naively. We find that even a coarse initializer greatly improves pose estimator accuracy, and improves overall computational efficiency. This approach proves to be effective also in challenging cases under more extreme pose variation. Experimental validation demonstrates that our method consistently achieves robust and accurate registration, enhancing the reliability of 2D/3D registration for clinical applications.

2D/3D Registration of Acetabular Hip Implants Under Perspective Projection and Fully Differentiable Ellipse Fitting

This paper presents a novel method for estimating the orientation and the position of acetabular hip implants in total hip arthroplasty using full anterior-posterior hip fluoroscopy images. Our method accounts for distortions induced in the fluoroscope geometry, estimating acetabular component pose by creating a forward model of the perspective projection and implementing differentiable ellipse fitting for the similarity of our estimation from the ground truth. This approach enables precise estimation of the implant's rotation (anteversion, inclination) and the translation under the fluoroscope induced deformation. Experimental results from both numerically simulated and digitally reconstructed radiograph environments demonstrate high accuracy with minimal computational demands, offering enhanced precision and applicability in clinical and surgical settings.

Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images

This work reports the empirical performance of an automated medical landmark detection method for predict clinical markers in hip radiograph images. Notably, the detection method was trained using a label-only augmentation scheme; our results indicate that this form of augmentation outperforms traditional data augmentation and produces highly sample efficient estimators. We train a generic U-Net-based architecture under a curriculum consisting of two phases: initially relaxing the landmarking task by enlarging the label points to regions, then gradually eroding these label regions back to the base task. We measure the benefits of this approach on six datasets of radiographs with gold-standard expert annotations.