Comparing densely calculated facial soft tissue depths for craniofacial reconstruction: Euclidean vs. perpendicular distances
Bibliographic record
Abstract
For surgical craniofacial reconstruction, preoperative planning may be limited by missing 3D skeletal geometry. In forensic sciences, ‘reconstruction’ models the 3D facial structure from skull geometries using soft-tissue depth mapping. This work investigates ‘reverse engineering’ the forensics’ TD Morpheus model to infer the bony shape from 3D facial surfaces by subtracting tissue depths inwards along the normal vectors. This approach using Euclidean tissue depths successfully estimated the upper and outermost skeletal regions (i.e. frontal, zygoma, and nasal bones) in 24 head CT scans, but concave skeletal surfaces were inaccurately evaluated where the face is convex yielding misshapen anatomy around the orbits and zygomatic arches. A perpendicular tissue depth algorithm was developed to probe inwards along the face’s normal vectors until contacting bone, demonstrating superior performance to the Euclidean depth approach. Accurate regional tissue depths achievable with this approach may provide a useful bridge to connect the 3D face and underlying skull geometry, with the potential for application in craniofacial reconstruction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".