Combined speckle variance optical coherence tomography and multiphoton microscopy for in vivo chick CAM imaging
Bibliographic record
Abstract
Combining optical coherence tomography (OCT) and multiphoton microscopy (MPM) can provide multimodal imaging of the microstructure of biological tissues. As a functional extension of conventional OCT, speckle variance OCT (SVOCT) can be applied to image microvasculature to improve the blood vessel visualization. In this paper, a combined SVOCT and MPM system is developed to visualize the chorioallantoic membrane (CAM) of a chick embryo, which contains extensive blood vessel network. Based on the different temporal decorrelation characteristics of the fluid flow and the surrounding stationary structure, SV-OCT enables enhanced contrast of fluid flow from the surrounding structure. As a result, SV-OCT can achieve detailed mapping of the CAM microvasculature at the tissue level. Meanwhile, MPM enables vascular imaging at the cellular level, where two-photon excitation fluorescence (TPEF) images fluorescein dye injected into the blood stream, and second harmonic generation (SHG) visualizes the collagen fiber structures in the vessel wall and the surrounding tissues. Therefore, the combined SV-OCT and MPM system provides complementary information about the microvasculature structures in the chick CAM. The combined system is shown to be a powerful tool for interpreting the microvasculature, by allowing the visualization of the blood vessel network in a relatively large field of view at the tissue level with SV-OCT, and by providing cellular-level information in local regions of interest with MPM.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".