Poster — Thur Eve — 49: Investigating the Effects of Motion on Texture within the Lung
Bibliographic record
Abstract
Automated methods using CT‐image‐based texture features have shown promise for segmentation of lung tumours. Combining CT with PET features for use in segmentation has been shown to improve segmentation accuracy compared to using either modality separately and has potential for use in accurate internal target volume definition in lung cancer. One issue of particular importance in lung tumor segmentation is the effect of motion on the measures extracted from PET and CT images. This aspect is under investigation using maximum intensity projections (MIPs) in addition to temporally gated CT, PET and un‐gated PET data sets. Preliminary results from 13 patients (9 diagnosed with lung cancer; 4 diagnosed with cancers outside the lung area, representing healthy lung tissue) show that with gated CT, PET and CT homogeneity, PET Entropy, and CT Coarseness are some of the strongest discriminators for use in classification with statistical distance measures of up to 2.0 between normal and abnormal tissue. The texture features from MIPs of 4 of the patients show that they present somewhat less discriminating feature values, as to be expected. Their usefulness within the context of segmentation remains to be investigated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".