scholarly journals Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin

2011 ◽  
Author(s):  
Sila Kurugol ◽  
Jennifer G. Dy ◽  
Milind Rajadhyaksha ◽  
Kirk W. Gossage ◽  
Jesse Weissmann ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Human Skin ◽  
Reflectance Confocal Microscopy ◽  
Automated Algorithm ◽  
Microscopy Images

Learning-based local quality assessment of reflectance confocal microscopy images for dermatology applications

2021 ◽  
Author(s):  
Miroslawa Sikorska ◽  
Andrzej Skalski ◽  
Marek Wodzinski ◽  
Alexander Witkowski ◽  
Giovanni Pellacani ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Quality Assessment ◽  
Reflectance Confocal Microscopy ◽  
Local Quality ◽  
Microscopy Images

scholarly journals Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alican Bozkurt ◽  
Kivanc Kose ◽  
Jaume Coll-Font ◽  
Christi Alessi-Fox ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
State Of The Art ◽  
Reflectance Confocal Microscopy ◽  
Convolutional Networks ◽  
Non Invasive ◽  
Extensive Training ◽  
Fold Reduction ◽  
Previous State ◽  
Microscopy Images ◽  
Field View

AbstractReflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4–5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved $$88.07\%$$ 88.07 % classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

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