Model-based correction of ultrasound image deformations due to probe pressure
Bibliographic record
Abstract
Freehand 3D ultrasound (US) consists in acquiring a US volume by moving a tracked conventional 2D probe over an area of interest. To maintain good acoustic coupling between the probe and the skin, the operator applies pressure on the skin with the probe. This pressure deforms the underlying tissues in a variable way across the excursion of the probe, which, in turn, leads to inconsistencies in the volume. To address this problem, this paper proposes a method to estimate the deformation field sustained by each image with respect to a reference deformation free image. The method is based on a 2D biomechanical model that takes into account the mechanical parameters of the tissues depicted in the image to predict a realistic deformation field. These parameters are estimated along with the deformation field such as to maximize the mutual information between the reference and the corrected images. The image is then corrected by applying the inverse deformation field. Preliminary validation was conducted with synthetic US images generated using a 3D biomechanical model. Results show that the proposed method improves image correction compared to a purely image-based method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".