Accuracy estimation in freehand ultrasound probe calibration
Why this work is in the frame
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Bibliographic record
Abstract
Three-dimensional, freehand ultrasound is an imaging technique that has seen increasing applications in computer assisted surgery. A key element of this technique is image calibration, in order to estimate a three-dimensional homogeneous transformation that maps the position of individual pixels from the ultrasound image coordinate to the ultrasound probe coordinate frames. The transformation is typically calculated through imaging a calibration phantom of known geometry, and solving for the transformation parameters (either in closed-form or iteratively). The calibration error achieved through this process is usually assumed to be constant for all the pixels in the image. In this paper, we propose a novel method to estimate the calibration accuracy for individual pixels within an ultrasound image by employing the Unscented Kalman Filter (UKF). Based on the variances of calibration parameters extracted by UKF, a mean square residual error is estimated for each individual pixel in the ultrasound image. We demonstrate that the calibration error could in fact significantly vary for different pixels in the image. This observation could potentially impact the image registration process in computer assisted surgery applications. The method has been validated through simulations and experiments.
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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 | 0.001 | 0.001 |
| 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