Cardiac right ventricular segmentation via point correspondence
Bibliographic record
Abstract
This study presents an approach to the segmentation of the right ventricle (RV) from a sequence of cardiac magnetic resonance (MR) images. Automatic delineation of the RV is difficult because of its complex morphology, thin and ill-defined borders, and the photometric similarities between the connected cardiac regions such as papillary muscles and heart wall. Further, geometric/photometric models are hard to build from a finite training set because of the significant differences in size, shape, and intensity between subjects. In this study, we propose to use a non-rigid registration method developed recently to obtain the point correspondence in a sequence of cine MR images. Given the segmentation on the first frame, the proposed method segments both endocardial and epicardial borders of the RV using the obtained point correspondence, and relaxes the need of a training set. The proposed method is evaluated quantitatively on common data set by comparison with manual segmentation, which demonstrated competitive results in comparison with recent methods.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".