Image Processing Techniques for Analysis of Myocardial Fibrosis and Related Cardiomyopathies in Cardiac Magnetic Resonance Imaging
Bibliographic record
Abstract
Myocardial fibrosis (MF) is a common feature of cardiac disease, characterized by excessive deposition of collagen (i.e., scar tissue) and expansion of the myocardial extracellular volume (ECV).This phenomenon contributes to cardiac dysfunction, promotes further cardiac disease, and has implication in preceding cardiac morbidity and mortality.The extent of myocardial MF can be analyzed globally (across the entire myocardial region) and/or regionally (across the fibrotic area exclusively) using cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhanced imaging or quantitative methods like native T1 and ECV mapping.CMR-based measurements of MF, native T1, and ECV allow for differentiation between various cardiac disease states and are shown to be clinically significant predictors of patient outcomes.However, in order to analyze tissue volumes or classify disease states, clinicians must first perform a manual tracing of the myocardial borders to define an initial region of interest (ROI), while regional MF quantification requires additional manual selection of a reference healthy myocardial tissue region.These manual processes are tedious, user-dependent, and highly prone to operator error, which can significantly confound resultant measures of T1, ECV and quantified MF tissue zones.Thus, alternative, minimally user-dependent techniques for MF, T1 and ECV quantification are appropriate.In this dissertation, several techniques for improving automated quantification of myocardial T1, ECV, and MF regions are presented.The proposed approaches presented in this document incorporate concepts from deep learning and image processing to achieve automated or semi-automated segmentation of the myocardium, MF, T1 and/or ECV in the left ventricle (LV) and left atrium (LA).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".