Collagen fibre organisation revealed with computational scattered light imaging and polarimetric second harmonic generation microscopy
Bibliographic record
Abstract
ABSTRACT Collagen forms dense fibre networks in the human body, with the organisation directly influencing tissue mechanics and function in health and disease. A good understanding of this relation requires proper imaging techniques for visualising the dense collagen network. Previously, computational scattered light imaging was employed as a fast and easy-to-implement technique to retrieve the orientation of multi-directional collagen fibres, but results were not yet validated quantitatively. In this study, we validate the in-plane orientations of collagen fibres determined with computational scattered light imaging by performing comparative measurements with polarimetric second harmonic generation microscopy on rat tendon and bone sections. For rat tendon, sections with and without hematoxylin-and-eosin staining, folded tendon layers, and obliquely cut sections were investigated. Similar fibre orientations were obtained with both techniques in both tissues, with the highest degree of similarity found for in-plane, unidirectional fibres in the tendon sections. The techniques were able to retrieve the orientations of multi-directional crossing collagen fibres in folded rat tendon layers, and results were found to be unaffected by staining. While polarimetric second harmonic generation microscopy provides high resolution and ultrastructural information on collagen, computational scattered light imaging provides large field of view measurements with micrometre resolution.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".