Segmentation and kinetic modeling of human arteries in PET/CT imaging
Bibliographic record
Abstract
Human arteries in elderly are subject to accumulation of fatty deposits forming atheromatous plaques. The plaque can develop into calcifications and provoke rigidity of the arteries or it can rupture and occlude peripheral arteries leading to vascular complications. Calcifications were measured with CT, and the high PET-FDG metabolism was associated with inflammation, a possible precursor of plaque rupture. In this work we present repeated measurements with CT and PET at T=0 and T=12 months later in three groups of elderly subjects: normal (N), hypercholesterolemic (H) and with stable angina (A). Methods: CT and PET-FDG images were first coregistered for calcification and inflammation localization, and then the arteries were segmented on CT and PET images using active contours, Chan-Vese, thresholding and factor analysis approaches. The segmentation allows to demonstrate if the calcification is accompanied with the inflammation and its progression during 12 months. Results: The calcifications were present at some portions of the arteries in all the subjects even in the normal group. The results showed that the most noticeable SUV changes between T0 and T12 were in non-calcified arteries of the normal group. In the three groups, the calcified arteries showed no significant differences between T0 and T12 while significant differences were observed for the non-calcified arteries. In non-calcified arteries at T0, N presents a low FDG uptake when compared to A and H. However, at T12, the SUV in N increased significantly when compared to A and reached values comparable to H. Conclusions: The quantitative analysis with FDG-PET/CT is efficient in the localization of the atheromatous plaque and evaluation of its progression instead of global evaluations with systemic inflammatory biomarkers. This technique is able to detect the progress of the disease within one year in order to prevent subsequent complications and to potentially assess the effects of medication.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".