Bibliographic record
Abstract
In this paper, we propose a new method for 2D/3D registration and report its experimental results. The method employs the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm to search for an optimal transformation that aligns the 2D and 3D data. The similarity calculation is based on Digitally Reconstructed Radiographs (DRRs), which are dynamically generated from the 3D data using a hardware-accelerated technique - Adaptive Slice Geometry Texture Mapping (ASGTM). Three bone phantoms of different sizes and shapes were used to test our method: a long femur, a large pelvis, and a small scaphoid. A collection of experiments were performed to register CT to fluoroscope and DRRs of these phantoms using the proposed method and two prior work, i.e. our previously proposed Unscented Kalman Filter (UKF) based method and a commonly used simplex-based method. The experimental results showed that: 1) with slightly more computation overhead, the proposed method was significantly more robust to local minima than the simplex-based method; 2) while as robust as the UKF-based method in terms of capture range, the new method was not sensitive to the initial values of its exposed control parameters, and has also no special requirement about the cost function; 3) the proposed method was fast and consistently achieved the best accuracies in all compared methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".