Intensity Robust Viscous Fluid Deformation Based Morphometry Using Regionally Adapted Mutual Information
Bibliographic record
Abstract
This paper describes an approach to fine scale non-rigid registration for mapping patterns of tissue volume loss in serial MRI studies of the brain. Specifically it addresses the important confound of diffuse tissue contrast changes which can influence local sub-voxel estimates of volume change. Such changes can be induced by neurodegenerative or neurodevelopmental processes, which not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. We derive an approach to the voxel-wise maximization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images. This provides a topology preserving map of local changes in volume between time points that is robust to regional changes in tissue contrast. Comparisons with current methodology are included showing that the approach provides a significant reduction in errors when tissue contrast varies locally between MRI acquisitions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".