Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image
Bibliographic record
Abstract
A segmentation method based on phase retardation measurements from polarization-sensitive optical coherence tomography (PS-OCT) is developed to differentiate the structural zones of articular cartilage. The organization of collagen matrix in articular cartilage varies over the different structural zones, generating different tissue birefringence. Analyzing the slope of the accumulated phase retardation at different depths can detect the variation in tissue birefringence and be used to segment the structural zones. The method is validated on phantoms composed of layers of different materials. Articular cartilage samples from adult swine are segmented with the method. The characteristics in each segmented zone are also examined by histology and high-resolution second-harmonic generation imaging, showing distinctive properties that match with the anatomical structure of articular cartilage. The segmentation algorithm is also applied on PS-OCT images acquired at multiple illumination angles, where the angular dependence of tissue birefringence in the deep zone is detected. This method offers a noninvasive imaging approach to differentiating the structural zones of articular cartilage, as well as a quantification approach based on the phase retardation measurements of PS-OCT. This method has great potential in studying depth-related progression of cartilage degeneration.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".