Reflectance Confocal Microscopy and Aging

Raphael, Anthony P.; Tokarz, Danielle; Ardigò, Marco; Prow, Tarl W.

doi:10.1007/978-3-662-47398-6_161

Cited by 2 publications

(3 citation statements)

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“…Nevertheless, the increase in exposure time will still cause damage to biological cells [11] and limit the frame rate. Nowadays, various fluorescence imaging techniques are widely used in the field of biological sciences, such as reflectance confocal microscopy (RCM) [12], two‐photon excitation (TPE) microscopy [13], multi‐photon excitation microscopy [14–16], light sheet microscopy [17–20], super‐resolution microscopy [21–24], structured illumination microscopy [25–27], etc. These microscopies are optimized for increased resolution and reduced phototoxicity.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based photodamage reduction on harmonic generation microscope at low‐level optical power

Shen,

Liao,

Tai

et al. 2023

Journal of Biophotonics

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The trade‐off between high‐quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep‐learning‐based power‐enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high‐power HGM images from low‐power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in‐vivo and ex‐vivo HGM imaging.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%

“…In skin diagnostics, harmonic generation microscope (HGM), optical coherence tomography (OCT) [29–32], and reflection confocal microscopy (RCM) [12, 33, 34] are commonly employed for biomedical imaging and non‐invasive virtual optical sectioning. RCM is based on the different refractive indices of cells and tissues themselves.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based photodamage reduction on harmonic generation microscope at low‐level optical power

Shen,

Liao,

Tai

et al. 2023

Journal of Biophotonics

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“…Such microstructures are cell membranes, melanosomes, collagen fibers, lipidic layers, keratin fibers, and intracellular organelles. 13 The closer the size of the organelle is to the wavelength of the light source and/or the higher its refractive index is compared with its surroundings, the brighter it appears. 2 …”

Section: Introduction To Reflectance Confocal Microscopymentioning

confidence: 99%

Automating reflectance confocal microscopy image analysis for dermatological research: a review

2022

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. Significance: Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient. Aim: This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images. Approach: A PubMed search was conducted with additional literature obtained from references lists. Results: The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal–epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images. Conclusions: RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.

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Reflectance Confocal Microscopy and Aging

Cited by 2 publications

References 58 publications

Deep learning‐based photodamage reduction on harmonic generation microscope at low‐level optical power

Deep learning‐based photodamage reduction on harmonic generation microscope at low‐level optical power

Automating reflectance confocal microscopy image analysis for dermatological research: a review

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