Autonomous Scanning and Motion-Aware Segmentation for Robotic Thyroid Reconstruction
Bibliographic record
Abstract
Ultrasound (US) imaging of thyroids, a non-invasive and real-time modality, has been one of the paramount techniques for nodule diagnosis. However, current procedures heavily rely on professional expertise. To mitigate this issue, we present an autonomous robotic system for real-time scanning and reconstruction of the thyroid from US images. We first develop an accurate PID controller scheme that integrates multimodal feedback from both image analysis and force sensors. This controller autonomously manages the thyroid scanning process using a robotic probe, without pre-planning the trajectory or any human intervention. Subsequently, we propose a real-time thyroid segmentation method termed TNSegNet, which exploits motion consistency across US sequences to improve segmentation coherence. Finally, we reconstruct and render the 3D thyroid according to the segmentation results. We perform extensive experiments to evaluate our proposed method and compare it with competing approaches on real-scanned thyroid datasets. Results demonstrate that our method achieves high-quality thyroid segmentation and reconstruction. Moreover, we showcase the application of our method to the scanning, segmentation, and reconstruction of thyroid nodules, where our method consistently delivers impressive performance. The code will be publicly available.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".