<title>Mapping of endoscopic images to object surfaces via ray-traced texture mapping for image guidance in neurosurgery</title>
Bibliographic record
Abstract
A major limitation of the use of endoscopes in minimally invasive surgery is the lack of relative context between the endoscope and its surroundings. The purpose of this work is to map endoscopic images to surfaces obtained from 3D preoperative MR or CT data, for assistance in surgical planning and guidance. To test our methods, we acquired pre- operative CT images of a standard brain phantom from which object surfaces were extracted. Endoscopic images were acquired using a neuro-endoscope tracked with an optical tracking system, and the optical properties of the endoscope were characterized using a simple calibration procedure. Registration of the phantom and CT images was accomplished using markers that could be identified both on the physical object and in the pre-operative images. The endoscopic images were rectified for radial lens distortion, and then mapped onto the extracted surfaces via a ray-traced texture- mapping algorithm, which explicitly accounts for surface obliquity. The optical tracker has an accuracy of about 0.3 mm, which allows the endoscope tip to be localized to within mm. The mapping operation allows the endoscopic images to be effectively 'painted' onto the surfaces as they are acquired. Panoramic and stereoscopic visualization and navigation of the painted surfaces may then be reformed from arbitrary orientations, that were not necessarily those from which the original endoscopic views were acquired.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".