New Protocol for Skin Landmark Registration in Image-Guided Neurosurgery
Bibliographic record
Abstract
BACKGROUND: Newer versions of the commercial Medtronic StealthStation allow the use of only 8 landmark pairs for patient-to-image registration as opposed to 9 landmarks in older systems. The choice of which landmark pair to drop in these newer systems can have an effect on the quality of the patient-to-image registration. OBJECTIVE: To investigate 4 landmark registration protocols based on 8 landmark pairs and compare the resulting registration accuracy with a 9-landmark protocol. METHODS: Four different protocols were tested on both phantoms and patients. Two of the protocols involved using 4 ear landmarks and 4 facial landmarks and the other 2 involved using 3 ear landmarks and 5 facial landmarks. Both the fiducial registration error and target registration error were evaluated for each of the different protocols to determine any difference between them and the 9-landmark protocol. RESULTS: No difference in fiducial registration error was found between any of the 8-landmark protocols and the 9-landmark protocol. A significant decrease (P < .05) in target registration error was found when using a protocol based on 4 ear landmarks and 4 facial landmarks compared with the other protocols based on 3 ear landmarks. CONCLUSION: When using 8 landmarks to perform the patient-to-image registration, the protocol using 4 ear landmarks and 4 facial landmarks greatly outperformed the other 8-landmark protocols and 9-landmark protocol, resulting in the lowest target registration error.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".