Automated quantification of myocardial infarction using graph cuts on contrast delayed enhanced magnetic resonance images.
Bibliographic record
Abstract
In this work, we propose a semi-automated myocardial infarction quantification method for cardiac contrast delayed enhancement magnetic resonance images (DE-MRI). Advantages of this method include that it reduces manual contouring of the left ventricle, obviates a remote myocardium region, and automatically distinguishes infarct, healthy and heterogeneous ("gray zone") tissue despite variability in intensity and noise across images. Quantitative evaluation results showed that the automatically determined infarct core and gray zone size have high correlation with that derived from the averaged results of the manual full width at half maximum (FWHM) methods (R(2)=0.99 for infarct core and gray zone size). Compared with the manual method, a much better reproducibility was achieved with the proposed algorithm and it shortens the evaluation time to one second per image, compared with 2-5 min per image for the manual method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".