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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.05702 |
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| _version_ | 1866917069172244480 |
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| author | Suh, Yehyun Li, Lin Plumley, Aric Zhou, Chaochao Moyer, Daniel Kang, Kongbin |
| author_facet | Suh, Yehyun Li, Lin Plumley, Aric Zhou, Chaochao Moyer, Daniel Kang, Kongbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pedicle Screw Pairing and Registration for Screw Pose Estimation from Dual C-arm Images Using CAD Models Suh, Yehyun Li, Lin Plumley, Aric Zhou, Chaochao Moyer, Daniel Kang, Kongbin Computer Vision and Pattern Recognition 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. |
| title | Pedicle Screw Pairing and Registration for Screw Pose Estimation from Dual C-arm Images Using CAD Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.05702 |