Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 30 Jun 2025.
Builds on
6 of 27 references.
References · 6
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2023Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgerycited by 11
- 2021Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cyclecited by 8
- 2021Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registrationcited by 4
- 2021Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgerycited by 3
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Citing papers per year: 2025: 1.
1 paper.