Constructing average models of quasi-spherical objects: application to corneal topographies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In medical imaging, it is now common to create 3D models of organs by ‘averaging’ several specimens obtained from different subjects. This requires a registration step to align the organs before averaging their shapes. In this paper, we present the difficult case of a quasi-spherical organ: the cornea. To cope with the lack of anatomical anchor points, we use a registration algorithm based on the minimisation of a global factor: the volume between the two surfaces to be registered. The cornea is a thin tissue layered by two (anterior and posterior) surfaces. Therefore, we actually introduce a third virtual surface to drive the two others. After registration using an iterative optimisation algorithm, anterior and posterior average surfaces are computed. Our study demonstrates that this matching step is crucial to correctly build and compare surfaces. Several clinical applications of this methodology are also presented to illustrate its efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it