A local contrast based approach to threshold segmentation for PET target volume delineation
Bibliographic record
Abstract
Current radiation therapy techniques, such as intensity modulated radiation therapy and three-dimensional conformal radiotherapy rely on the precise delivery of high doses of radiation to well-defined volumes. CT, the imaging modality that is most commonly used to determine treatment volumes cannot, however, easily distinguish between cancerous and normal tissue. The ability of positron emission tomography (PET) to more readily differentiate between malignant and healthy tissues has generated great interest in using PET images to delineate target volumes for radiation treatment planning. At present the accurate geometric delineation of tumor volumes is a subject open to considerable interpretation. The possibility of using a local contrast based approach to threshold segmentation to accurately delineate PET target cross sections is investigated using well-defined cylindrical and spherical volumes. Contrast levels which yield correct volumetric quantification are found to be a function of the activity concentration ratio between target and background, target size, and slice location. Possibilities for clinical implementation are explored along with the limits posed by this form of segmentation.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".