Towards markerless intraoperative tracking of deformable spine tissue
File(s)
Accepted version
Author(s)
Type
Conference Paper
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.
Date Issued
2026-01-01
Date Acceptance
2025-09-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2026, 15968, pp.627-637
ISBN
9783032051134
ISSN
0302-9743
Publisher
Springer
Start Page
627
End Page
637
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
15968
Copyright Statement
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
MICCAI 2025
Publication Status
Published
Start Date
2025-09-23
Finish Date
2025-09-27
Coverage Spatial
Daejeon, South Korea
Date Publish Online
2025-09-21