Graph Cuts-based Segmentation of Alveolar Bone in Ultrasound Imaging
Bibliographic record
Abstract
Alveolar bone is a part of the periodontium complex supporting the teeth. Conventional radiography and cone-beam computed tomography are currently used to image the alveolar bones. Recently, ionizing radiation-free ultrasound has shown promising potential to image dento-periodontium. However, the ability to visualize alveolar bones in ultrasound images is a challenge for the dentists who are novice to ultrasonography. This study proposes a semi-automated technique to segment alveolar bone by using a multi-label graph cuts optimization approach, where the K-means clustering of intensity values was used in constructing the initial graph. A homomorphic filter was employed as a preprocessing step to de-noise the ultrasound data. The approach was evaluated by over 15 ultrasound images acquired from fresh porcine specimens. Four quantitative evaluators, namely Dice coefficient, sensitivity, specificity, and Hausdorff distance were measured from the proposed method and the manual ground truth by an expert orthodontist. The inter-rater and intra-rater variabilities were also calculated using the delineations by three raters with different levels of experience. The study has demonstrated that the proposed segmentation method provides consistent, reliable, and accurate results among raters and thus has potential to be used as a tool to help dentists to delineate alveolar bones for further analysis.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".