Tracking the major temporal arcade in retinal fundus images
Bibliographic record
Abstract
Accurate detection of the major temporal arcade (MTA) in retinal fundus images could assist in localization of various anatomical features of the retina, such as the optic nerve head (ONH) and fovea, as well as in detection of certain types of retinopathy. In this paper, we present a novel automated tracking algorithm to obtain a skeleton image representing only the MTA, by detection of the vascular tree using Gabor filters, detection of the center of the ONH using phase portrait analysis, and morphological image processing. The methods were trained and tested using two independent sets of 20 retinal images each. The results were evaluated in terms of the mean distance to the closest point (MDPC) computed for each tracked MTA as compared to its corresponding hand-drawn trace. The test results indicate a low average MDCP error of approximately 2 pixels per MTA skeleton. The proposed algorithm should assist in detection and diagnosis of diseases such as proliferative diabetic retinopathy and retinopathy of prematurity, as well as in localization of the ONH and fovea.
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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 | 0.001 | 0.000 |
| 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 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".