Skin layer structure characterization via multi-contrast polarization-sensitive optical coherence tomography
Notice bibliographique
Résumé
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].
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».