Automated fibrosis segmentation from wideband post‐contrast T1∗$$ {T}_1^{\ast } $$ mapping in an animal model of ischemic heart disease with implantable cardioverter‐defibrillators
Bibliographic record
Abstract
PURPOSE: maps with an ICD present and compare it to manually tuned thresholding of synthetic LGE images with an ICD present and standard LGE without an ICD present. METHODS: Seven swine received an ischemia-reperfusion myocardial infarction and were imaged at 3 T 4-5 weeks post-infarct with and without an ICD. Mapping-based thresholding was performed using synthetic LGE and artifact-corrected cluster-thresholding methods, both employing connected component filtering. Standard pixel signal intensity thresholding was performed on the conventional LGE without an ICD. Volumetric accuracy is relative to conventional LGE and Dice similarity between SynLGE and cluster-based segmentations were evaluated. RESULTS: No statistical significance was observed between LGE volumes without an ICD and both SynLGE and artifact-corrected cluster-threshold volumes with an ICD, when using connected component filtering. Additionally, Dice alignment between SynLGE and cluster-thresholding was high for healthy myocardium (0.96), dense scar (0.83), and dense scar union gray zone (0.91) when artifact correction and connected component filtering were implemented. CONCLUSION: maps holds promise for a reproducible approach to scar segmentation in the presence of ICDs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".