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Record W4212834445 · doi:10.1117/12.2613256

Skin layer structure characterization via multi-contrast polarization-sensitive optical coherence tomography

2022· article· en· W4212834445 on OpenAlexaff
Sina Maloufi, Xin Zhou, Shuo Tang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOptical coherence tomographyBirefringenceSkin cancerMedical imagingMedicineMaterials scienceRadiologyBiomedical engineeringOpticsCancerInternal medicine

Abstract

fetched live from OpenAlex

Skin cancer is one of the most prevalent types of cancer in the world, with a steadily increasing incidence rate and associated health burden [1, 2]. While it is generally treatable when detected, survival rates decrease dramatically as the disease progresses – highlighting the importance of early detection [3]. Unfortunately, the current gold standard in diagnosing skin cancers involves taking biopsies followed by histopathology, which is invasive and time consuming. Some studies have shown that the majority of biopsies ordered by primary care providers were found to be benign, meaning that biopsies are often performed when there is no cancer present [4]. Given this, there is a great interest in developing noninvasive diagnostic tools for skin imaging. Optical coherence tomography (OCT) is an imaging modality that is particularly well suited for this area, owing to its ability to provide high-resolution (3-15 μm) volumetric data at a penetration depth of up to 1.5 mm [5]. In a manner analogous to ultrasound, it provides cross-sectional images which can be comparable to histology slides [6]. However, conventional intensity-based OCT only provides structural information with no functional contrast, and as a result it has encountered difficulty in diagnosing specific cancers such as melanoma [7]. Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT which can characterize polarization properties such as the birefringence of tissue samples – birefringence specifically occurs in fibrous structures such as collagen [8]. Several groups have investigated birefringence in skin tissue using PS-OCT, but little work has been done with the more recently defined degree of polarization uniformity (DOPU) contrast. The functional contrast provided by DOPU PS-OCT imaging can provide localized, depth-resolved information on the polarization scrambling properties of samples. A specific example of this is in ophthalmic imaging, where DOPU contrast in PS-OCT imaging demonstrated the ability to segment the retinal pigment epithelium – a layer otherwise hard to differentiate in intensity-based OCT imaging [9]. To our knowledge, very few if any groups have investigated PS-OCT imaging with DOPU contrast in understanding the layered-structure of skin. We have recently investigated the DOPU in skin tissue phantoms and reported that DOPU is sensitive to surface roughness – an important factor in differentiating skin cancers from benign lesions. Our group has a previously reported PS-OCT system that can simultaneously acquire reflectance, phase retardation (birefringence), and DOPU images that we aim to use in this study to expand on our previous work and further investigate polarization properties in skin in vivo [10].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.210
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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