Segmentation Uncertainty Quantification in Cardiac Propagation Models
Why this work is in the frame
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Bibliographic record
Abstract
A key part of patient-specific cardiac simulations is segmentation, yet the impact of this subjective and errorprone process hasn't been quantified in most simulation pipelines.In this study we quantify the dependence of a cardiac propagation model on from segmentation variability.We used statistical shape modeling and polynomial Chaos (PC) to capture segmentation variability dependence and applied its affects to a propagation model.We evaluated the predicted local activation times (LATs) an body surface potentials (BSPs) from two modeling pipelines: an EIkonal propagation model and a surfacebased fastest route model.The predicted uncertainty due to segmentation shape variability was distributed near the base of the heart and near high amplitude torso potential regions.Our results suggest that modeling pipelines may have to accommodate segmentation errors if regions of interest correspond to high segmentation error.Further, even small errors could proliferate if modeling results are used to to feed further computations, such as ECGI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it