A universal calibration framework for mixed-reality assisted surgery
Bibliographic record
Abstract
BACKGROUND: Mixed-reality-assisted surgery has become increasingly prominent, offering real-time 3D visualization of target anatomy such as tumors. These systems facilitate translating preoperative 3D surgical plans to the patient's body intraoperatively and allow for interactive modifications based on the patient's real-time conditions. However, achieving sub-millimetre accuracy in mixed-reality (MR) visualization and interaction is crucial to mitigate device-related risks and enhance surgical precision. OBJECTIVE: Given the critical role of camera calibration in hologram-to-patient anatomy registration, this study aims to develop a new device-agnostic and robust calibration method capable of achieving sub-millimetre accuracy, addressing the prevalent uncertainties associated with MR camera-to-world calibration. METHODS: We utilized the precision of surgical navigation systems (NAV) to address the hand-eye calibration problem, thereby localizing the MR camera within a navigated surgical scene. The proposed calibration method was integrated into a representative surgery system and subjected to rigorous testing across various 2D and 3D camera trajectories that simulate surgeon head movements. RESULTS: The calibration method demonstrated positional errors as low as 0.2 mm in spatial trajectories, with a standard error also at 0.2 mm, underscoring its robustness against camera motion. This accuracy complies with the accuracy and stability requirements essential for surgical applications. CONCLUSION: The proposed fiducial-based hand-eye calibration method effectively incorporates the accuracy and reliability of surgical navigation systems into MR camera systems used in intraoperative applications. This integration facilitates high precision in surgical navigation, proving critical for enhancing surgical outcomes in mixed-reality-assisted procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".