Mathematical methods for 2D-3D cardiac image registration
Bibliographic record
Abstract
We propose a mathematical formulation aimed at intensity-based slice-to-volume registration,\naligning a cross-sectional slice of a 3D volume to a 2D image. The approach is\nflexible and can accommodate various regularization schemes, similarity measures, and\noptimizers. We evaluate the framework by registering 2D and 3D cardiac magnetic resonance\n(MR) images obtained in vivo, aimed at image-guided surgery applications that\nutilise real-time MR imaging as a visualization tool. Rigid-body and affine transformations\nare used to validate the parametric model. Target registration error (TRE),\nJaccard, and Dice indices are used to evaluate the algorithm and demonstrate the accuracy\nof the registration scheme on both simulated and clinical data. Registration with the\naffine model appeared to be more robust than the rigid model in controlled registration\nexperiments. By simply extending the rigid model to an affine model, alignment of the\ncardiac region generally improved, without the need for complex dissimilarity measures\nor regularizers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".