Label‐free volumetric imaging of conjunctival collecting lymphatics ex vivo by optical coherence tomography lymphangiography
Bibliographic record
Abstract
We employ optical coherence tomography (OCT) and optical coherence microscopy (OCM) to study conjunctival lymphatics in porcine eyes ex vivo. This study is a precursor to the development of in vivo imaging of the collecting lymphatics for potentially guiding and monitoring glaucoma filtration surgery. OCT scans at 1300 nm and higher-resolution OCM scans at 785 nm reveal the lymphatic vessels via their optical transparency. Equivalent signal characteristics are also observed from blood vessels largely free of blood (and devoid of flow) in the ex vivo conjunctiva. In our lymphangiography, vessel networks were segmented by compensating the depth attenuation in the volumetric OCT/OCM signal, projecting the minimum intensity in two dimensions and thresholding to generate a three-dimensional vessel volume. Vessel segmentation from multiple locations of a range of porcine eyes (n = 21) enables visualization of the vessel networks and indicates the varying spatial distribution of patent lymphatics. Such visualization provides a new tool to investigate conjunctival vessels in tissue ex vivo without need for histological tissue processing and a valuable reference on vessel morphology for the in vivo label-free imaging studies of lymphatics to follow.
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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 | 0.001 | 0.001 |
| 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".