P5C-4 Image Guidance Using Camera and Ultrasound Images
Bibliographic record
Abstract
Previous work of estimating needle trajectories using un-calibrated camera-based tracking ("touchless" needle guide) is extended here to real ultrasound imaging. Using 4D ultrasound, this technique can be used as a needle guide during percutaneous procedures. A pair of miniature cameras captures images and the ultrasound system captures a volume as the needle moves through the workspace. The 2D position and orientation of the needle is calculated from the camera images and the 3D position of the needle is estimated from the ultrasound volume. A database is created from these values and a non-parametric learning algorithm determines the best model to the data. Then, in practice, the model can be used to estimate the projected needle trajectory within the ultrasound volume with only camera images of the needle above the skin surface. This study achieved an average error of 0.94 mm in position and 3.93deg in orientation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".