SU‐E‐J‐85: Anthropomorphic Development for Intermodality Deformation Algorithms Validation
Bibliographic record
Abstract
PURPOSE: Radiation therapy is often based on a single treatment plan calculated on patient's anatomy at the time of the simulation scan. Deformation algorithms offer the possibility to register initial treatment plan on a daily CBCT. This way, the planning can be adapted to the evolution of patient anatomy. Validation of deformable image registration algorithms (DRA) ideally requires the use of phantoms offering some deformation possibilitiesMethods: An anthropomorphic, pelvic phantom was built to test volume variation (bladder), deformation of contours (prostate) and translation (all organs). Algorithms must be able to perform intermodality registration. Therefore, images were acquired for both CT and CBCT. The phantom has been created in a way to allow total control of the deformation amplitude. Each of the three types of deformations studied were realized independently and scanned in a manner to have the same initial and deformed images set for each modality. RESULTS: Two algorithm systems were use to compare their efficiency; an open-source software, a toolbox for registration that offers parameter adjustment and a commercial system with limited control for user. The phantom provides us usable images for DRA validation. For a 2 cm mass center organ translation, the first one reduced 98% of the distance while the other only performed 60%. For a 100 ml volume variation, we get 88% and 62%. CONCLUSIONS: Comparison of each intermodal deformation registration performed by the two algorithm systems show how control on parameters improves registration quality. DRA allow the initial planning adaptation on different deformations which occur in human body.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".