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

Abstract Reflectance 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...

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Autores principales:	Alican Bozkurt, Kivanc Kose, Jaume Coll-Font, Christi Alessi-Fox, Dana H. Brooks, Jennifer G. Dy, Milind Rajadhyaksha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1a4b35450a944762b79c7f8cbc8691e3
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spelling	oai:doaj.org-article:1a4b35450a944762b79c7f8cbc8691e32021-12-02T16:04:35ZSkin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention10.1038/s41598-021-90328-x2045-2322https://doaj.org/article/1a4b35450a944762b79c7f8cbc8691e32021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90328-xhttps://doaj.org/toc/2045-2322Abstract Reflectance 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.Alican BozkurtKivanc KoseJaume Coll-FontChristi Alessi-FoxDana H. BrooksJennifer G. DyMilind RajadhyakshaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alican Bozkurt Kivanc Kose Jaume Coll-Font Christi Alessi-Fox Dana H. Brooks Jennifer G. Dy Milind Rajadhyaksha Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
description	Abstract Reflectance 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.
format	article
author	Alican Bozkurt Kivanc Kose Jaume Coll-Font Christi Alessi-Fox Dana H. Brooks Jennifer G. Dy Milind Rajadhyaksha
author_facet	Alican Bozkurt Kivanc Kose Jaume Coll-Font Christi Alessi-Fox Dana H. Brooks Jennifer G. Dy Milind Rajadhyaksha
author_sort	Alican Bozkurt
title	Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
title_short	Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
title_full	Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
title_fullStr	Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
title_full_unstemmed	Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
title_sort	skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1a4b35450a944762b79c7f8cbc8691e3
work_keys_str_mv	AT alicanbozkurt skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT kivanckose skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT jaumecollfont skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT christialessifox skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT danahbrooks skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT jennifergdy skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention AT milindrajadhyaksha skinstratadelineationinreflectanceconfocalmicroscopyimagesusingrecurrentconvolutionalnetworkswithattention
_version_	1718385193554804736

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

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