Multidimensional en-Face OCT imaging of the retina
Bibliographic record
Abstract
Fast T-scanning (transverse scanning, en-face) was used to build B-scan or C-scan optical coherence tomography (OCT) images of the retina. Several unique signature patterns of en-face (coronal) are reviewed in conjunction with associated confocal images of the fundus and B-scan OCT images. Benefits in combining T-scan OCT with confocal imaging to generate pairs of OCT and confocal images similar to those generated by scanning laser ophthalmoscopy (SLO) are discussed in comparison with the spectral OCT systems. The multichannel potential of the OCT/SLO system is demonstrated with the addition of a third hardware channel which acquires and generates indocyanine green (ICG) fluorescence images. The OCT, confocal SLO and ICG fluorescence images are simultaneously presented in a two or a three screen format. A fourth channel which displays a live mix of frames of the ICG sequence superimposed on the corresponding coronal OCT slices for immediate multidimensional comparison, is also included. OSA ISP software is employed to illustrate the synergy between the simultaneously provided perspectives. This synergy promotes interpretation of information by enhancing diagnostic comparisons and facilitates internal correction of movement artifacts within C-scan and B-scan OCT images using information provided by the SLO channel.
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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 | 0.001 | 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) | 0.002 | 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".