Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916819379421184 |
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| author | Daly, Connor Marconi, Elettra Riva, Marco Ekanayake, Jinendra Elson, Daniel S. Baena, Ferdinando Rodriguez y |
| author_facet | Daly, Connor Marconi, Elettra Riva, Marco Ekanayake, Jinendra Elson, Daniel S. Baena, Ferdinando Rodriguez y |
| contents | Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-mounted tracking devices, markerless tracking can reduce operating time and complexity. However, its use has been limited to cadaveric studies. This paper introduces the first real-world clinical RGB-D dataset for spine surgery and develops SpineAlign, a system for capturing deformation between preoperative and intraoperative spine states. We also present an intraoperative segmentation network trained on this data and introduce CorrespondNet, a multi-task framework for predicting key regions for registration in both intraoperative and preoperative scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Markerless Intraoperative Tracking of Deformable Spine Tissue Daly, Connor Marconi, Elettra Riva, Marco Ekanayake, Jinendra Elson, Daniel S. Baena, Ferdinando Rodriguez y Computer Vision and Pattern Recognition Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-mounted tracking devices, markerless tracking can reduce operating time and complexity. However, its use has been limited to cadaveric studies. This paper introduces the first real-world clinical RGB-D dataset for spine surgery and develops SpineAlign, a system for capturing deformation between preoperative and intraoperative spine states. We also present an intraoperative segmentation network trained on this data and introduce CorrespondNet, a multi-task framework for predicting key regions for registration in both intraoperative and preoperative scenes. |
| title | Towards Markerless Intraoperative Tracking of Deformable Spine Tissue |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23657 |