Automated myocardial segmentation of extra-cellular volume mapping cardiac magnetic resonance images using fully convolutional neural networks
Bibliographic record
Abstract
Extra-cellular volume (ECV) mapping cardiac magnetic resonance (CMR) imaging allows for the characterization of expanded myocardial extracellular space, a common feature of myocardial fibrosis (MF). Quantification of MF is feasible using ECV mapping techniques; however, prior manual delineation of the endocardial and epicardial borders is required. In this study, we propose a method for automated myocardial delineation of ECV maps using convolutional neural networks (CNNs). We compare two methods based on the standard U-Net and the U-Net++ architectures using a five-fold cross validation on basal, mid, and apical short-axis ECV maps of the left ventricle (LV) in 73 patients with ischemic (n=38) or dilated (n=35) cardiomyopathies. The standard U-Net and U-Net++ -based architectures yielded DSC metrics of 87.61% and 87.89%, respectively, against manual contours derived by an expert. Precision and recall were reported >85% and relative error <12% for both CNNs. The U-Net++ architecture outperformed the standard U-Net on the order of 1-2% for all metrics. An inter-operator variability analysis was performed on a subset of myocardial contours derived by three operators. The inter-operator analysis demonstrated significant differences in the distribution of myocardial ECV values among three operators as per the Kruskal-Wallis H-test (average pair-wise P-value = 0.040), but operator differences failed to show significance against U-Net++ or standard U-Net (average pair-wise P-value 0.055 and 0.060, respectively). Correlation of global ECV improved for operators against U-Net++ (𝜌=0.88) and against standard U-Net (𝜌=0.877) compared to correlation of global ECV values between all operators (ρ=0.828).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".