Robot-assisted Breast Ultrasound Scanning Using Geometrical Analysis of the Seroma and Image Segmentation
Bibliographic record
Abstract
In this paper, we propose a robotic ultrasound imaging method that scans the breast in two separate phases to acquire high-quality ultrasound images. Our proposed system controls five Degrees of Freedom (DoFs) of the robot that hold an ultrasound probe to perform precise scanning. This system finds the desired trajectory based on geometrical analysis of the target inside the breast in a pre-scan phase and uses this information to control the probe in a post-scan phase. The proposed method updates the desired values of rotational and translational movement of the probe in the post-scan by calculating the center of mass of segmented target in each acquired frame and the average of image confidence map. The proposed method has been tested experimentally on a plastisol phantom. Given a specific trajectory, the position and orientation of the probe have been controlled at each point of the trajectory. The experiments’ result shows us that our proposed visual servoing algorithm successfully controls the probe to look at target tissue and is fast enough for use in a robotic control loop.
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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 | 0.001 | 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) | 0.001 | 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".