Deformable registration using scale space keypoints
Bibliographic record
Abstract
In this paper, we describe a new methodology for keypoint-based affine and deformable medical image registration. This fast and computationally efficient method is automatic and does not rely on segmentation of images. The keypoint pixels used in this technique are extreme points in the scale space and are characterized by descriptor vectors which summarize the intensity gradient profile of the surrounding pixels. For each of the keypoints in the scene image, a corresponding keypoint is identified in the model image using the feature space nearest neighbor criteria. For deformable registration, B-splines are used to extrapolate a regular deformation grid for all of the pixels in the scene image based on the relative displacement vectors of the corresponding pairs. This approach results in a fast and accurate registration in the brain MRI images (an average target registration error of less than 2mm was acquired). We have also studied the affine registration problem in the liver ultrasound and brain MRI images and have acquired acceptable registrations using a mean square solution for affine parameters based on only around 30 corresponding keypoint pairs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".