Real-time Motion Stabilization with B-mode Ultrasound Using Image Speckle Information and Visual Servoing
Bibliographic record
Abstract
We develop visual servo control to stabilize the image of moving soft tissue in B-mode ultrasound (US) imaging. We define the target region in a B-mode US image, and automatically control a robot to manipulate a an US probe by minimizing the difference between the target and the most recently acquired US image. We exploit tissue speckle information to compute the relative pose between the probe and the target region. In-plane motion is handled by image region tracking and out-of-plane motion recovered by speckle tracking using speckle decorrelation. A visual servo control scheme is then applied to manipulate the US probe to stabilize the target region in the live US image. In a first experiment involving only translational motion, an US phan-In a first experiment involving only translational motion, an US phan tom was moved by one robot while stabilizing the target with a second robot holding the US probe. In a second experiment, large six-degree-of-freedom (DOF) motions were manually applied to an US phantom while a six-DOF medical robot was controlled automatically to compensate for the probe displacement. The obtained results support the hypothesis that automated motion stabilization shows promise for a variety of US-guided medical procedures such as prostate cancer brachytherapy.
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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 | 0.000 | 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 | 0.000 |
| 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 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".