Automatic tongue surface extraction from three-dimensional ultrasound vocal tract images
Bibliographic record
Abstract
Three-dimensional (3D/4D) ultrasound (US) imaging of the tongue has emerged as a useful instrument for articulatory studies. However, extracting quantitative measurements of the shape of the tongue surface remains challenging and time-consuming. In response to these challenges, this paper documents and evaluates the first automated method for extracting tongue surfaces from 3D/4D US data. The method draws on established methods in computer vision, and combines image phase symmetry measurements, eigen-analysis of the image Hessian matrix, and a fast marching method for surface evolution towards the automatic detection of the sheet-like surface of the tongue amidst noisy US data. The method was tested on US recordings from eight speakers and the resulting automatically extracted tongue surfaces were generally found to lie within 1 to 2 mm from their corresponding manually delineated surfaces in terms of mean-sum-of-distances error. Further experiments demonstrate that the accuracy of 2D midsagittal tongue contour extraction is also improved using 3D data and methods. This is likely because the additional information afforded by 3D US compared to 2D US images strongly constrains the possible location of the midsagittal contour. Thus, the proposed method seems appropriate for immediate practical use in the analysis of 3D/4D US recordings of the tongue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".