Fast and efficient image registration based on gradient orientations of minimal uncertainty
Bibliographic record
Abstract
There exist a wide variety of time sensitive contexts (e.g. image-guided neurosurgery (IGNS)), whereby image registration is required to be both fast and accurate if it is to be adopted clinically. Many sampling techniques have been proposed to speed up the registration process but these often come at the expense of accuracy (e.g. random). In this paper, we describe a fast and accurate multi-modal registration framework based on matching gradient orientations at locations of minimal gradient magnitude uncertainties in a coarse-to-fine manner. In the context of IGNS, the method was shown to perform with accuracies below 2mm using 2% of the total voxels when tested on the 14 cases of the publicly available BITE dataset [1]. For rigid registration between MRI and CT brain images on the RIRE dataset [2], the quantitative results demonstrate that the proposed approach can employ highly reduced sampling rates (e.g. 0.05% of the voxels in the image) while still yielding a median registration error inferior to 1mm [3]. In the context of the non-rigid registration of inter-patient MRI brain volumes, the proposed approach is evaluated with a publicly available dataset, and achieves comparable accuracy to the top performing methods but with only one sixth of the processing time [4]. While the results are promising, there are remaining challenges associated with existing sampling techniques, as well as limitations in the existing validation frameworks for registration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".