Characterizing refractive index and thickness of biological tissues using combined multiphoton microscopy and optical coherence tomography
Bibliographic record
Abstract
We present a noninvasive method for characterizing the refractive index (RI) and thickness distribution in biological tissues using a combined multiphoton microscopy (MPM) and optical coherence tomography (OCT) system. Tissue layers are distinguished by the MPM and OCT images, and the RI and thickness of each layer are determined by analyzing the co-registered MPM and OCT images. The precision of this method is evaluated on four standard samples which are water, air, immersion oil and cover glass. Precision of within ~1% error compared to reference values is obtained. Biological tissue measurement is demonstrated on fish cornea. Three layers are detected, which are identified as the epithelium and stroma I and II of the cornea. The corresponding RI of each layer is measured to be ~1.446-1.448, 1.345-1.372, and 1.392-1.436, respectively. The difference of RI in the three layers correlates with the tissue compositions including cells in epithelium, large collagen fiber bundles in stroma I, and small collagen fibers in stroma II. The combined MPM/OCT technique is shown to be able to distinguish tissue layers through biochemically specific contrasts and measure RI and thickness of tissue layers at different depths.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".