Sci—Thur PM: Imaging — 02: Repeated landmark use for patient‐to‐image registration reduces fiducial registration error in patient‐to‐image mapping in image guided neurosurgery
Bibliographic record
Abstract
The patient‐to‐image mapping for an image guided neurosurgical procedure is traditionally determined through the identification of 9 anatomical landmark pairs on both diagnostic preoperative magnetic resonance images and the patient in the operating room. In this study we investigate the effect of using an increased number of point pairs on the mean fiducial registration error of the landmark registration. This was first evaluated in the lab on a custom built precision milled linear testing apparatus. It was used in conjunction with an optical tracking system and a tracked surgical pointer. A volume of 700 points was registered and showed a plateau of the mean fiducial registration error when a large number of points were used. This was then extended to the operating room where 5 sets of the 9 anatomical landmarks were used for registration and compared to the registration only done with 1 set. A significant improvement (one tailed t‐test) was seen in five of the six cases showing an improvement in patient‐to‐image registration with no significant addition to operation duration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".