Initial investigation of an automatic registration algorithm for surgical navigation
Why this work is in the frame
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Bibliographic record
Abstract
The procedure required for registering a surgical navigation system prior to use in a surgical procedure is conventionally a time-consuming manual process that is prone to human errors and must be repeated as necessary through the course of a procedure. The conventional procedure becomes even more time consuming when intra-operative 3D imaging such as the C-arm cone-beam CT (CBCT) is introduced, as each updated volume set requires a new registration. To improve the speed and accuracy of registering image and world reference frames in image-guided surgery, a novel automatic registration algorithm was developed and investigated. The surgical navigation system consists of either Polaris (Northern Digital Inc., Waterloo, ON) or MicronTracker (Claron Technology Inc., Toronto, ON) tracking camera(s), custom software (Cogito running on a PC), and a prototype CBCT imaging system based on a mobile isocentric C-arm (Siemens, Erlangen, Germany). Experiments were conducted to test the accuracy of automatic registration methods for both the MicronTracker and Polaris tracking cameras. Results indicate the automated registration performs as well as the manual registration procedure using either the Claron or Polaris camera. The average root-mean-squared (rms) observed target registration error (TRE) for the manual procedure was 2.58 +/- 0.42 mm and 1.76 +/- 0.49 mm for the Polaris and MicronTracker, respectively. The mean observed TRE for the automatic algorithm was 2.11 +/- 0.13 and 2.03 +/- 0.3 mm for the Polaris and MicronTracker, respectively. Implementation and optimization of the automatic registration technique in Carm CBCT guidance of surgical procedures is underway.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it