A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo

2016 ◽  
Author(s):  
Kivanc Kose ◽  
Christi Alessi-Fox ◽  
Melissa Gill ◽  
Jennifer G. Dy ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Machine Learning ◽  
Confocal Microscopy ◽  
Skin Lesions ◽  
Reflectance Confocal Microscopy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Morphological Patterns

scholarly journals Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223682 ◽  
Author(s):  
Ulf Dahlstrand ◽  
Rafi Sheikh ◽  
Cu Dybelius Ansson ◽  
Khashayar Memarzadeh ◽  
Nina Reistad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Tissue Classification

In vivo reflectance confocal microscopy imaging of melanocytic skin lesions: Consensus terminology glossary and illustrative images

2007 ◽  
Vol 57 (4) ◽  
pp. 644-658 ◽  
Author(s):  
Alon Scope ◽  
Cristiane Benvenuto-Andrade ◽  
Anna-Liza C. Agero ◽  
Josep Malvehy ◽  
Susana Puig ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Skin Lesions ◽  
Reflectance Confocal Microscopy ◽  
Microscopy Imaging ◽  
Confocal Microscopy Imaging

The combined role of clinical, reflectance confocal microscopy and dermoscopy applied to chronic discoid cutaneous lupus and subacutus lupus erythematosus: A case series and literature review

Lupus ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 125-133
Author(s):  
Sara Mazzilli ◽  
Laura Vollono ◽  
Laura Diluvio ◽  
Elisabetta Botti ◽  
Gaetana Costanza ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Lupus Erythematosus ◽  
Cutaneous Lupus Erythematosus ◽  
Clinical Manifestations ◽  
Case Series ◽  
Skin Lesions ◽  
Reflectance Confocal Microscopy ◽  
Discoid Lupus Erythematosus ◽  
Cutaneous Lupus

Main subtypes of cutaneous lupus erythematosus are represented by acute, subacute cutaneous, intermittent and chronic cutaneous lupus erythematosus. Discoid lupus erythematosus represents the most common phenotype of chronic cutaneous lupus erythematosus. The spectrum of clinical manifestations mirrors that of several and distinct histopathological features. Such variability among different CLE subtypes is also observed at dermoscopy. Dermoscopy is nowadays considered an additional valuable method for skin lesions assessment in general dermatology, following and completing the well-known clinical diagnostic steps, such as medical history and clinical examination. In vivo reflectance confocal microscopy (RCM) is a non-invasive imaging tool able to assess the epidermis and upper dermis producing high resolution (horizontal ∼1.25 μm, vertical ∼5 μm), en face tissue sections used for melanocytic and inflammatory evaluation. In this study, we reported dermoscopic and RCM features about 9 patients affected by subacute and chronic lupus erythematosus retrospectively analyzed.

Reflectance confocal microscopy for the in vivo detection of Treponema pallidum in skin lesions of secondary syphilis

2009 ◽  
Vol 60 (4) ◽  
pp. 639-642 ◽  
Author(s):  
Marina Venturini ◽  
Raffaella Sala ◽  
Diego Semenza ◽  
Amerigo Santoro ◽  
Fabio Facchetti ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Treponema Pallidum ◽  
Skin Lesions ◽  
Secondary Syphilis ◽  
Reflectance Confocal Microscopy ◽  
In Vivo Detection

scholarly journals Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology

2022 ◽  
Vol 11 (2) ◽  
pp. 429
Author(s):  
Ana Maria Malciu ◽  
Mihai Lupu ◽  
Vlad Mihai Voiculescu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Confocal Microscopy ◽  
Image Quality Assessment ◽  
Skin Diseases ◽  
Learning Technologies ◽  
Reflectance Confocal Microscopy ◽  
Imaging Method

Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence’s impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.

scholarly journals Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marissa D’Alonzo ◽  
Alican Bozkurt ◽  
Christi Alessi-Fox ◽  
Melissa Gill ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Machine Learning ◽  
Confocal Microscopy ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Reflectance Confocal Microscopy ◽  
Pigmented Lesions ◽  
Architectural Patterns

AbstractReflectance confocal microscopy (RCM) is a non-invasive imaging tool that reduces the need for invasive histopathology for skin cancer diagnoses by providing high-resolution mosaics showing the architectural patterns of skin, which are used to identify malignancies in-vivo. RCM mosaics are similar to dermatopathology sections, both requiring extensive training to interpret. However, these modalities differ in orientation, as RCM mosaics are horizontal (parallel to the skin surface) while histopathology sections are vertical, and contrast mechanism, RCM with a single (reflectance) mechanism resulting in grayscale images and histopathology with multi-factor color-stained contrast. Image analysis and machine learning methods can potentially provide a diagnostic aid to clinicians to interpret RCM mosaics, eventually helping to ease the adoption and more efficiently utilizing RCM in routine clinical practice. However standard supervised machine learning may require a prohibitive volume of hand-labeled training data. In this paper, we present a weakly supervised machine learning model to perform semantic segmentation of architectural patterns encountered in RCM mosaics. Unlike more widely used fully supervised segmentation models that require pixel-level annotations, which are very labor-demanding and error-prone to obtain, here we focus on training models using only patch-level labels (e.g. a single field of view within an entire mosaic). We segment RCM mosaics into “benign” and “aspecific (nonspecific)” regions, where aspecific regions represent the loss of regular architecture due to injury and/or inflammation, pre-malignancy, or malignancy. We adopt Efficientnet, a deep neural network (DNN) proven to accurately accomplish classification tasks, to generate class activation maps, and use a Gaussian weighting kernel to stitch smaller images back into larger fields of view. The trained DNN achieved an average area under the curve of 0.969, and Dice coefficient of 0.778 showing the feasibility of spatial localization of aspecific regions in RCM images, and making the diagnostics decision model more interpretable to the clinicians.

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