Segmentation of Trabecular Jaw Bone on Cone Beam <scp>CT</scp> Datasets
Bibliographic record
Abstract
BACKGROUND: The term bone quality is often used in a dentomaxillofacial context, for example in implant planning, as bone density and bone structure have been linked to primary implant success. PURPOSE: This research aimed to investigate the performance of adaptive thresholding of trabecular bone in cone beam CT (CBCT) images. The segmentation quality was assessed for different imaging devices and upper and lower jaws. MATERIALS AND METHODS: Four jaws were scanned with eight CBCT scanners and one micro-CT device. Images of the jaws were spatially aligned with the micro-CT images. Two volumes of interest for each jaw were manually delineated. Trabecular bone in the volumes of interest in the micro-CT images was segmented so that the micro-CT images could serve as high-resolution ground truth images. The volumes of interest in the CBCT images were segmented using both global and adaptive thresholding. RESULTS: Segmentation was significantly better for the lower jaw than for the upper jaw. Differences in performance between the scanners were significant for both jaws. Adaptive thresholding performed significantly better in segmenting the bone structure out of CBCT images. CONCLUSIONS: When assessing jaw bone structure, the observer should always choose adaptive thresholding. It remains a challenge to identify the optimal threshold selection for the structural assessment of jaw bone.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".