Poster — Thur Eve — 33: A Dose‐Based Metric for Evaluation of Image Registration Accuracy
Bibliographic record
Abstract
Current approaches for the valuation of image registration accuracy rely on comparison of calculated point displacements with measured motion of manually identified anatomical landmarks or contours. In the context of using image registration to map dose distributions, the interpretation of a landmark analysis in terms of a dose error is not readily obvious. In this work we propose a new method to evaluate image registration accuracy based on a dose mapping error. The dose error is calculated by comparing two different dose mapping approaches which use the same deformation vectors but one scores the energy deposited on the target geometry while the other scores energy deposition on a deformed reference geometry. Any error in the deformation vectors will lead to a discrepancy between these geometries and a difference in the warped dose distribution. The dose warping error was evaluated on a set of inhale and exhale images for a lung patient. Multiple image registrations were performed with different levels of registration accuracy. At each landmark point the dose warping error was computed as well as a simplified dose error calculated from the landmark error and the dose gradient. A comparison of dose mapping error and landmark error revealed that the latter is not sufficient to predict the accuracy of the dose warping and factors such as the smoothness of the deformations must be considered. The proposed dose mapping error can be readily applied to determine the accuracy of image registrations used in treatment planning applications.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".