Snake-based interactive tooth segmentation for 3D mandibular meshes
Bibliographic record
Abstract
Mandibular meshes segmented from computerized tomography (CT) images contain rich information of the dentition conditions, which impairs the performance of shape completion algorithms relying on such data, but can benefit virtual planning for oral reconstructive surgeries. To locate the alveolar process and remove the dentition area, we propose a tooth segmentation method including a preprocessing step using non-rigid registration, an active contour model, and constructive solid geometry (CSG) operations. An easy-to-use interactive tool is developed, allowing users to adjust the tooth crown contour position. A validation study and a comparison study were conducted for method evaluation. In the validation study, we removed teeth for 28 models acquired from Vancouver General Hospital (VGH) and ran a shape completion test. Regarding 95th percentile Hausdorff distance (HD95), using edentulous models produced significantly better predictions of the premorbid shapes of diseased mandibles than using models with inconsistent dentition conditions(Z = -2.484, p = 0.01). The volumetric Dice score (DSC) shows no significant difference. In the second study, we compared the proposed method to manual removal in terms of manual processing time, symmetric HD95, and symmetric root mean square deviation (RMSD). The result indicates that our method reduced the manual processing time by 40% on average and approached the accuracy of manual tooth segmentation. It is promising to warrant further efforts towards clinical usage. This work forms the basis of a useful tool for coupling jaw reconstruction and restorative dentition for patient treatment planning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".