Automated seed detection and three‐dimensional reconstruction. II. Reconstruction of permanent prostate implants using simulated annealing
Bibliographic record
Abstract
We present an algorithm, based on simulated annealing, for automatic seed matching and three-dimensional spatial coordinate reconstruction using either three radiographic films or three fluoroscopic images taken from different perspectives. The matching problem is defined in the framework of combinatorial optimization, which allows robust reconstruction in presence of calibration imprecision, patient movements, and isometric distortions. Furthermore, by using a global criterion to select the correct matching, we evade common problems of the three-film method and its variants in presence of noise. The algorithm has been tested on 112 clinical cases and 100 simulated implants and used clinically on more than 100 cases. Simulated implants were reconstructed with an average error of 0.21 mm. For clinical cases, comparison of the precision is performed between results obtained with this new method and results obtained using the three-film technique. Compared to the latter technique, the reconstruction precision was improved in 62% of the clinical cases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".