Unsupervised cardiac PET image segmentation
Bibliographic record
Abstract
Automatic, unsupervised image segmentation plays an important role in medical imaging, but remains a challenging task in positron emission tomography (PET) due to unpredictable object shapes and inconsistent image quality resulting from noise and sampling artifacts. The main objective of this work is to develop a segmentation method for the mouse myocardium PET images based on deformable models. Two moving curves, one from inside of the left ventricle and one from the outside of the heart will be deformed to track heart boundaries. More precisely, topology constraints are incorporated to the energy functional governing the evolution of the contours to avoid any collision while allowing them to compete against each other until stabilization. First, we locate the heart, which is the region of interest (ROI) for our study, using level sets with high internal energy initialized from the extremities of the image. It is followed by an optimal thresholding and the application of the mean shift clustering algorithm to locate the center of the left ventricle region. This is where a second contour (interior contour) is initialized. The coupled contours allow to detect the correct myocardial boundaries and compute a number of useful quantities such as the ejection-fraction of the left ventricle and the myocardium wall thickness. The model was applied successfully to the automatic segmentation of the PET images of a mouse myocardium as measured by the Sherbrooke LabPET scanner.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".