Automatic generation of anatomy‐based MLC fields in aperture‐based IMRT
Bibliographic record
Abstract
We have developed an algorithm to automatically generate anatomy-based MLC fields. For each beam, a first field is adjusted to the projection of the target in a beam's eye view, allowing subsequent fields to be derived from this conformal field by removing the overlapping surface of each organ at risk, respectively. The projections are based on a surface sampling of the anatomical structures. On top of the MLC mechanical constraints, verification constraints are imposed on the MLC segments, in order to get reliable dosimetry using a commercial dose calculation engine. Thus, in each direction, the aperture's cross-section must be greater than a specified threshold, in our case 2 cm. Furthermore, junctions are not tolerated in order to avoid underdosage, for instance from the tongue-and-groove effect. The use of such MLC fields simplifies the verification process. The performance of the algorithm is illustrated for head and neck, thorax and prostate cases. Only a fraction of a second of CPU time is required to perform the segmentation for each beam.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".