3D reconstruction of lower anterior teeth from CBCT images: automatic segmentation with manual refinements
Bibliographic record
Abstract
OBJECTIVE: To develop a well-detailed and reproducible tooth segmentation method, when quantifying tooth volumetric measurements is needed. MATERIAL AND METHODS: This was an in vitro study in which lower incisors and canines of five patients were 3D reconstructed by means of an automatic segmentation with manual refinements process. All the images were obtained using a 0.3-mm voxel size CBCT imaging. The software utilized was the ITK-SNAP®. The primary outcomes were the intra-rater and inter-rater reliabilities and the respective measurement errors. RESULTS: The intra-rater reliability was excellent, with a mean measurement error of 4.16%. The inter-rater reliability was good, with a mean measurement error of 7.11%. Accuracy assessment was not possible, as the assessed teeth were not extracted. CONCLUSIONS: Although the described method is reliable, tooth volumetric error measurements may become significant, depending on the assessed situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".