Epidermal-dermal segmentation and polarization feature analysis using advanced Jones matrix optical coherence tomography
Bibliographic record
Abstract
Jones matrix optical coherence tomography (JM-OCT) is a form of polarization-sensitive OCT (PS-OCT) that allows for the simultaneous and high-quality in vivo capture of multiple polarization-based imaging contrasts. Our system allows for the capture of high-sensitivity structural OCT, degree of polarization uniformity (DOPU), and birefringence images. Segmenting the epidermal-dermal junction is a topic of high interest in OCT and dermatology. While others have explored this with traditional OCT, no other groups have used this type of segmentation with JM-OCT. We believe that combining a reliable segmentation procedure with the robust and localized characterization provided by JM-OCT can help fully utilize the strengths of both techniques and allow for a better characterization of the skin layers. Here, we utilize JM-OCT to assess the skin properties of healthy volunteers. Using high-sensitivity OCT, we quantified the epidermal thickness of various locations in vivo and were able to segment the epidermis, dermis, and stratum corneum in thick skin. Polarization imaging is sensitive to specific structures in the skin, such as collagen and melanin, and we were able to quantify the depolarization, and birefringence caused by these structures in different skin layers. In thick skin, localized polarization results showed an average DOPU of 0.89 in the dermis compared to an average of 0.79 in the stratum corneum. However, in both thick and thin skin, DOPU was lower overall in the epidermis compared to the dermis, indicating that most observed depolarization occurs due to the structure of the stratum corneum. Birefringence was found to be higher in the dermis than the epidermis in both cases.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".