Accuracy and sensitivity of finite element model‐based deformable registration of the prostate
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Evaluate the accuracy and the sensitivity to contour variation and model size of a finite element model-based deformable registration algorithm for the prostate. METHODS AND MATERIALS: Two magnetic resonance images (MRIs) were obtained for 21 prostate patients with three implanted markers. A single observer contoured the prostate and markers and performed blinded recontouring of the first MRI. A biomechanical-model based deformable registration algorithm, MORFEUS, was applied to each dataset pair, deforming the second image (B) to the first image (A). The residual error was calculated by comparing the center of mass (COM) of the markers with the predicted COM. Sensitivity to contour variation was calculated by deforming B to the repeat contour of A (A2). The sensitivity to the model size was calculated by reducing the number of nodes (B', A', A2') and repeating the analysis. RESULTS: The average residual error of the registration for B to A and B to A2 was 0.22 cm (SD: 0.08 cm) and 0.24 cm (SD: 0.09 cm), respectively. The average residual error of the registration of B' to A' and B' to A2' was 0.22 cm (SD: 0.10 cm) and 0.25 cm (SD: 0.10 cm), respectively. The average time to run MORFEUS on the standard and reduced model was 3606 s (SD: 7788 s) and 56 s (SD: 16 s), respectively. CONCLUSIONS: The accuracy of the algorithm, equal to the image voxel size, is not affected by intraobserver contour variability or model size. Reducing the model size significantly increases algorithm efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it