Accuracy estimation in freehand ultrasound probe calibration
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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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".