Automated Method for Clinic and Morphologic Analysis of Bones Using Implicit Modeling Technique
Bibliographic record
Abstract
Bone morphology and moprhometric estimation provide important and useful information for computed assisted-surgery, follow-up evaluation and personalized prosthesis design. Obtaining this data without any operator supervision or setting remains a practical goal. We present here an automated method that estimates clinic, anatomic and morphometric parameters based on bone-mesh representation. The method uses 2 steps. In the first one, the bone of interest is introduced as an implicit function modeling its morphology as a quadric surface. This function blends together basic geometries such as spheres, cylinders, quadratics and superquadratics and approximates its external shape. Given a mesh representation of a patient-bone, Levenber-Marquardt optimization technique computes Cartesian coordinates of the basic geometries. In second step, heuristic plans use these spatial data to locate, through the mesh representation, punctual landmarks. In order to compute subsequently complex clinic and anatomic landmarks relatives to axes, curves, surfaces, and regions, compound-heuristic plans are dressed using implicit parameters and previous punctual landmarks. Each plan is expressed as a energy-cost function that involves geometric, radial and normal terms. The method has been successfully used to locate clinic, anatomic and morphometric parameters of femur bone. Validation of the technique is performed with qualitative and quantitative procedures. A total of 9 femurs are reconstructed using a retroprojection technique. In all models, the method converges to the same parameters with acceptable clinical accuracy. As automated method, this schema presents practical advantage and remains sufficiently general to be applied to other bones and tracks most of anatomic parameters.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".