Automated multimodel segmentation and tracking for AR-guided open liver surgery using scene-aware self-prompting
Our scene-aware re-prompting strategy provides a trade-off between segmentation accuracy and temporal resolution, thus addressing the requirements of real-time AR-guided open liver surgery. The integration of complementary models resulted in robust and accurate segmentation in a complex, real-world surgical settings.
Authors
- Serouj Khajarian (ORCID: https://orcid.org/0009-0009-1986-6916)
- Konstantin Holzapfel
- Stefanie Remmele (ORCID: https://orcid.org/0009-0009-9760-5412)
- Oliver Amft (ORCID: https://orcid.org/0000-0001-6811-3659)
- Michael Schwimmbeck (ORCID: https://orcid.org/0009-0003-0349-8844)
- Christopher Auer
- J. Schmidt
Institutions
- University of Freiburg (DE)
- University of Applied Sciences Landshut (DE)
- Hahn-Schickard-Gesellschaft für angewandte Forschung (DE)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1007/s11548-025-03381-6
- Citations
- 2
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- Bayerisches Staatsministerium für Wissenschaft und Kunst