3D segmentation of artery 18F-FDG PET/CT images in elderly utilizing affinity propagation
Bibliographic record
Abstract
We present in this study a semi-automatic procedure to segment artery PET images in elderly subjects with atherosclerosis. The inflammation in the artery is a precursor of atheromatous plaque detachment and obstruction of blood vessels in the brain or the heart. CT images allow calcification detection in the arteries, and dynamic PET images with 18F-FDG allow to calculate glucose metabolism in the arteries to ultimately detect the inflammation. The goal of the artery image segmentation was to correlate the calcification on CT images to the artery metabolism on the PET images. First, all the artery images were delineated in each slice on the non-enhanced CT images with reference to anatomic atlases, and we used the active contours to locate the corresponding PET artery images at early time frames. All the artery images on PET were corrected for partial volume effect then each slice of the PET images was segmented with the algorithm of affinity propagation (AP). We introduced the segmentation of the dynamic histogram which is more accurate than segmenting image frames independently. The 3D reconstruction of the segmented arteries will be used in a future work to correlate glucose uptake to artery calcification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".