Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine
Bibliographic record
Abstract
This paper presents on-going research that addresses uncertainty along the Z-axis in image-guided surgery, for applications to large surgical workspaces, including those found in veterinary medicine. Veterinary medicine lags human medicine in using image guidance, despite MR and CT data scanning of animals. The positional uncertainty of a surgical tracking device can be modeled as an octahedron with one long axis coinciding with the depth axis of the sensor, where the short axes are determined by pixel resolution and workspace dimensions. The further a 3D point is from this device, the more elongated is this long axis, and the greater the uncertainty along Z of this point's position, in relation to its components along X and Y. Moreover, for a triangulation-based tracker, its position error degrades with the square of distance. Our approach is to use two or more Micron Trackers to communicate with each other, and combine this feature with flexible positioning. Prior knowledge of the type of surgical procedure, and if applicable, the species of animal that determines the scale of the workspace, would allow the surgeon to pre-operatively configure the trackers in the OR for optimal accuracy. Our research also leverages the open-source Image-guided Surgery Toolkit (IGSTK).
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".