A point-selection algorithm based on spatial-stiffness analysis of rigid registration
Bibliographic record
Abstract
OBJECTIVE: We propose a model of shape-based registration that leads to a task-specific algorithm for preoperatively selecting a set of model registration points. MATERIALS AND METHODS: We performed five sets of computer simulations using registration points generated by our algorithm and two noise amplification index (NAI) algorithms on the basis of the research of Simon 20. We used several different bone surface models (distal radius, proximal femur and tibia) computed from CT images of patient volunteers. The number of registration points used varied between 6 and 30. RESULTS: Our algorithm was faster than the NAI-based algorithms by factors of approximately 4 and 200. It had equal or better performance in terms of target registration error (TRE) when compared with the other algorithms. Our simulations also showed that point selection can have a large effect on TRE behavior; in particular, poor point selection does not necessarily decrease TRE as more registration points are added. CONCLUSIONS: Our point-selection algorithm produces model registration points with similar or better TRE behavior than the NAI-based algorithms we tested, and it does so with significantly less computation time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".