<title>Procedure to detect anatomical structures in optical fundus images</title>
Why this work is in the frame
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Bibliographic record
Abstract
We present an overview of the design and test of an image processing procedure for detecting all important anatomical structures in color fundus images. These structures are the optic disk, the macula and the retinal network. The algorithm proceeds through five main steps: (1) automatic mask generation using pixels value statistics and color threshold, (2) visual image quality assessment using histogram matching and Canny edge distribution modeling, (3) optic disk localization using pyramidal decomposition, Hausdorff-based template matching and confidence assignment, (4) macula localization using pyramidal decomposition and (5) bessel network tracking using recursive dual edge tracking and connectivity recovering. The procedure has been tested on a database of about 40 color fundus images acquired from a digital non-mydriatic fundus camera. The database is composed of images of various types (macula- and optic disk-centered) and of various visual quality (with or without abnormal bright or dark regions, blurred, etc).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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