Sensorless adaptive optics optical coherence tomography for two photon excited fluorescence mouse retinal imaging
Bibliographic record
Abstract
Two-Photon Excited Fluorescence (TPEF) is a common modality for volumetric imaging within a biological sample. The molecule-specific contrast of TPEF imaging of the retina enables novel in vivo studies of disease and retina physiology. Furthermore, retinal studies can be aided by imaging the relevant cells, which can be fluorescently labelled with fluorophores such as GFP. The multiphoton absorption suppresses the out-of-focus background signal and improves with the axial sectioning without a confocal aperture in the optical detection path. Imaging the retina with near infrared (NIR) light is ideal since the retina contain visual pigments that are sensitive to visible wavelengths and NIR light has less scattering within biological tissue than shorter wavelengths. However, high pulse energy is required to generate the TPEF, yet minimizing the incident exposure energy is required for non-invasive imaging. TPEF signal intensity increases quartically with the spot size, which leads to high sensitivity to aberrations that distribute the energy deposition of the focused light1. For retinal imaging, decreasing the spot size requires the imaging beam to fill a larger area of the eye, which corresponds to an increase in the aberration amplitudes and often results in low TPEF. Recent research has demonstrated improvements to the TPEF signal for retinal imaging using AO for aberrations correction2,3,4.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".