Computed tomography image restoration using convex-potential 3-D Markov random fields [for knee joint prostheses design]
Bibliographic record
Abstract
In order to design and manufacture custom-fitted prostheses of the knee joint, one must perform a very accurate geometric reconstruction of the bone surface from computed tomography images. This communication deals with the image restoration used to improve the reconstructed images. In the Bayesian context of maximum a posteriori estimation, edge-preserving Markov random fields as an a priori model for the images have proven to give very good results. To avoid dealing with nonconvex optimization, we adopt the elegant method proposed by Brette and Idier [1996]. This approach leads to a very efficient single site update algorithm and an analytical formulation can be found for convex edge-preserving potentials. Moreover, we propose to define a three dimensional Markov random field to take into account the geometry of computed tomography. The resulting restoration allows good recovery of the sharp discontinuities between the bone and soft tissues and the accuracy is significantly improved by the three dimensional model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".