A Generalizable Framework for Domain-Specific Nonrigid Registration: Application to Cardiac Ultrasound
Bibliographic record
Abstract
Many applications of nonrigid point set registration could benefit from a domain-specific model of allowed deformations. We observe that registration methods using mixture models optimize a differentiable log-likelihood function and are thus amenable to gradient-based optimization. In theory, this allows optimization of any transformations that are expressed as arbitrarily nested differentiable functions. In practice such optimization problems are readily handled with modern machine learning tools. We demonstrate, in experiments on synthetic data generated from a model of the left cardiac ventricle, that complex nested transformations can be robustly optimized using this approach. As a realistic application, we also use the method to propagate the model through an entire cardiac ultrasound sequence. We conclude that this approach, which works with both points and oriented points, provides an easily generalizable framework in which complex, application-specific transformation models may be constructed and optimized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".