Graph-based tracking of the tongue contour in ultrasound sequences with adaptive temporal regularization
Why this work is in the frame
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Bibliographic record
Abstract
We propose a graph-based approach for semi-automatic tracking of the human tongue in 2D+time ultrasound image sequences. We construct a graph capturing the intra- (spatial) and inter-frame (temporal) relationships between the dynamic contour vertices. Tongue contour tracking is formulated as a graph-labeling problem, where each vertex is labeled with a displacement vector describing its motion. The optimal displacement labels are those minimizing a multi-label Markov random field energy with unary, pairwise, and ternary potentials, capturing image evidence and temporal and smoothness regularization, respectively. The regularization strength is designed to adapt to the reliability of images features. Evaluation based on real clinical data and comparative analyses with existing approaches demonstrate the accuracy and robustness of our method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it