Visualizing positional uncertainty in freehand 3D ultrasound
Bibliographic record
Abstract
The freehand 3D ultrasound technique relies on position sensors attached to the probe to register the location of each image to a 3D space. However, the imprecision of the position sensors reduces the reliability of estimated image locations. In this paper, we propose a novel method to compute the positional uncertainty of an image plane. First, we use rigid body point-based registration to compute the error produced by each pixel of the image during the tracking. The Target Registration Error (TRE) is used to compute the covariance matrix of errors at each pixel position. This covariance matrix is then decomposed as a 3D orientation error, in the x, y and z directions. Considering a volume around the image, we introduce the Image Plane Crossing Probability (IPCP) to determine the probability that the plane passes through each voxel. The result is a point cloud probability around the image plane, where each voxel contains the crossing probability and the contribution of each direction of the error. Finally, a simple volume rendering technique is used to visualize the uncertainty of the plane position. The results are validated in two steps. The first step is a Monte Carlo simulation to validate the estimate of the TRE covariance for the tracking errors. The second step simulates TRE errors on a plane and validates the associated positional uncertainty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".