3D reconstruction of the scapula from biplanar radiographs
Bibliographic record
Abstract
Access to 3D bone models is critical for applications ranging from pre-operative planning to biomechanics studies. This work presents a method for 3D reconstruction of the scapula from biplanar radiographs, which is based on the combination of a parametric model approach in conjunction with a Moving Least Squares (MLS) deformation technique. A parametric scapula model was created by fitting geometric primitives (with their descriptive parameters) to the CT reconstruction of a dry scapula. These geometric primitives were then used to define a set of handles which allow the user to control the as-rigid-as-possible deformation of the template model in real-time, until optimal correspondence between the actual X-ray images and the retro-projection of the deformed model. When applied to 10 dry scapulae, the presented method allowed obtaining reconstructions which were on average within 1mm of the CT-derived model at scapula regions of interest. Morphological parameters such as the glenoid's dimensions and orientation were determined with errors of 1° and less than 1mm, on average. This is of great interest as the current methods used in clinical practice, which are based on 2D-CT, are subject to uncertainties of the order of 5° for glenoid version. This method is of particular interest as it further reduces our dependence to CT for 3D reconstruction of bones and clinical parameter estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".