Self-Supervised Feature Detection and 3D Reconstruction for Real-Time Neuroendoscopic Guidance
The self-supervised feature detection method advances capabilities for vision-based guidance and augmented visualization of target structures in neuroendoscopic procedures. The approach could enhance the accuracy and precision of neurosurgery to improve patient outcomes.
Authors
- Ryan P. Lee (ORCID: https://orcid.org/0000-0002-2517-9559)
- Craig Jones (ORCID: https://orcid.org/0000-0002-0629-3006)
- Prasad Vagdargi (ORCID: https://orcid.org/0000-0002-2740-6604)
- Stephen Z. Liu (ORCID: https://orcid.org/0000-0001-9157-4074)
- Gregory D. Hager (ORCID: https://orcid.org/0000-0002-6662-9763)
- Alejandro Sisniega (ORCID: https://orcid.org/0000-0003-2030-2711)
- Jeffrey H. Siewerdsen (ORCID: https://orcid.org/0000-0001-6644-1317)
- Mark G. Luciano (ORCID: https://orcid.org/0000-0003-1456-9559)
- Patrick A. Helm (ORCID: https://orcid.org/0009-0009-3734-4951)
- Ali Uneri (ORCID: https://orcid.org/0000-0003-3419-1805)
- Junghoon Lee (ORCID: https://orcid.org/0000-0002-3004-8625)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Johns Hopkins University Applied Physics Laboratory (US)
- Medtronic (Ireland) (IE)
- Medtronic (United States) (US)
- Johns Hopkins Hospital (US)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1109/tbme.2025.3538683
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00