Analysis of low-scattering regions in optical coherence tomography: applications to neurography and lymphangiography
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Bibliographic record
Abstract
Analysis of semi-transparent low scattering biological structures in optical coherence tomography (OCT) has been actively pursued in the context of lymphatic imaging, with most approaches relying on the relative absence of signal as a means of detection. Here we present an alternate methodology based on spatial speckle statistics, utilizing the similarity of a distribution of given voxel intensities to the power distribution function of pure noise, to visualize the low-scattering biological structures of interest. In a human tumor xenograft murine model, we show that these correspond to lymphatic vessels and nerves; extensive histopathologic validation studies are reported to unequivocally establish this correspondence. The emerging possibility of OCT lymphangiography and neurography is novel and potentially impactful (especially the latter), although further methodology refinement is needed to distinguish between the visualized lymphatics and nerves.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| 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) | 0.000 | 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 it