Line-scanning SD-OCT for in-vivo, non-contact, volumetric, cellular resolution imaging of the human cornea and limbus
Bibliographic record
Abstract
In-vivo , non-contact, volumetric imaging of the cellular and sub-cellular structure of the human cornea and limbus with optical coherence tomography (OCT) is challenging due to involuntary eye motion that introduces both motion artifacts and blur in the OCT images. Here we present the design of a line-scanning (LS) spectral-domain (SD) optical coherence tomography system that combines 2 × 3 × 1.7 µm (x, y, z) resolution in biological tissue with an image acquisition rate of ∼2,500 fps, and demonstrate its ability to image in-vivo and without contact with the tissue surface, the cellular structure of the human anterior segment tissues. Volumetric LS-SD-OCT images acquired over a field-of-view (FOV) of 0.7 mm × 1.4 mm reveal fine morphological details in the healthy human cornea, such as epithelial and endothelial cells, sub-basal nerves, as well as the cellular structure of the limbal crypts, the palisades of Vogt (POVs) and the blood microvasculature of the human limbus. LS-SD-OCT is a promising technology that can assist ophthalmologists with the early diagnostics and optimal treatment planning of ocular diseases affecting the human anterior eye.
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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 | 0.000 | 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) | 0.001 | 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 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".