Framework for quantitative three-dimensional choroidal vasculature analysis using optical coherence tomography
Why this work is in the frame
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Bibliographic record
Abstract
Choroidal vasculature plays an important role in the pathogenesis of retinal diseases, such as myopic maculopathy, age-related macular degeneration, diabetic retinopathy, central serous chorioretinopathy, and ocular inflammatory diseases. Current optical coherence tomography (OCT) technology provides three-dimensional visualization of the choroidal angioarchitecture; however, quantitative measures remain challenging. Here, we propose and validate a framework to segment and quantify the choroidal vasculature from a prototype swept-source OCT (PLEX Elite 9000, Carl Zeiss Meditec, USA) using a 3×3 mm scan protocol centered on the macula. Enface images referenced from the retinal pigment epithelium were reconstructed from the volumetric data. The boundaries of the choroidal volume were automatically identified by tracking the choroidal vessel feature structure over the depth, and a selective sliding window was applied for segmenting the vessels adaptively from attenuation-corrected enface images. We achieved a segmentation accuracy of 96% ± 1% as compared with manual annotation, and a dice coefficient of 0.83 ± 0.04 for repeatability. Using this framework on both control (0.00 D to -2.00 D) and highly myopic (-8.00 D to -11.00 D) eyes, we report a decrease in choroidal vessel volume (p<0.001) in eyes with high myopia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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