Towards Markerless Intraoperative Tracking of Deformable Spine Tissue

2025/06/30 by Daly, Connor, Marconi, Elettra, Riva, Marco +3 · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2506.23657

Abstract

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.

Citations

• Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle
• Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery
• nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• PointNet: Deep Learning on Point Sets for 3D Classification and\n Segmentation
• Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms

Cited by

• End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery

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