An unsupervised machine learning method for delineating stratum corneum in reflectance confocal microscopy stacks of human skin in vivo

2016 ◽  
Author(s):  
Alican Bozkurt ◽  
Kivanc Kose ◽  
Christi A. Fox ◽  
Jennifer Dy ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Machine Learning ◽  
Confocal Microscopy ◽  
Stratum Corneum ◽  
Human Skin ◽  
Reflectance Confocal Microscopy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning

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

Channel head extraction based on fuzzy unsupervised machine learning method

Geomorphology ◽  
2021 ◽  
pp. 107888
Author(s):  
Jian Wu ◽  
Haixing Liu ◽  
Zhe Wang ◽  
Lei Ye ◽  
Min Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning

scholarly journals An unsupervised machine learning method for assessing quality of tandem mass spectra

2012 ◽  
Vol 10 (Suppl 1) ◽  
pp. S12 ◽  
Author(s):  
Wenjun Lin ◽  
Jianxin Wang ◽  
Wen-Jun Zhang ◽  
Fang-Xiang Wu
Keyword(s):  
Machine Learning ◽  
Mass Spectra ◽  
Tandem Mass ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Tandem Mass Spectra

scholarly journals Real-Time Evidence of In Vivo Leukocyte Trafficking in Human Skin by Reflectance Confocal Microscopy

2001 ◽  
Vol 117 (2) ◽  
pp. 384-386 ◽  
Author(s):  
Salvador González ◽  
Robert Sackstein ◽  
R. Rox Anderson ◽  
Milind Rajadhyaksha
Keyword(s):  
Confocal Microscopy ◽  
Real Time ◽  
Human Skin ◽  
Reflectance Confocal Microscopy ◽  
Leukocyte Trafficking

scholarly journals Automated segmentation of skin strata in reflectance confocal microscopy depth stacks

2015 ◽  
Author(s):  
Samuel Hames ◽  
Marco Ardigò ◽  
H. Peter Soyer ◽  
Andrew P. Bradley ◽  
Tarl W. Prow
Keyword(s):  
Confocal Microscopy ◽  
Stratum Corneum ◽  
Skin Diseases ◽  
Age Groups ◽  
Reflectance Confocal Microscopy ◽  
Papillary Dermis ◽  
Test Set ◽  
Viable Epidermis ◽  
En Face

Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.

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