Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning.

Autor:	Huttunen MJ; Photonics Laboratory, Physics Unit, Tampere University, Tampere, Finland.; These authors contributed equally to this work.; mikko.huttunen@tuni.fi., Hristu R; Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.; These authors contributed equally to this work., Dumitru A; Department of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.; These authors contributed equally to this work., Floroiu I; Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.; Faculty of Medical Engineering, Politehnica University of Bucharest, Bucharest, Romania., Costache M; Department of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.; mariana.costache@umfcd.ro., Stanciu SG; Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania.
Jazyk:	angličtina
Zdroj:	Biomedical optics express [Biomed Opt Express] 2019 Dec 10; Vol. 11 (1), pp. 186-199. Date of Electronic Publication: 2019 Dec 10 (Print Publication: 2020).
DOI:	10.1364/BOE.11.000186
Abstrakt:	Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo . Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained fixed tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained convolutional neural networks. Our results suggest that deep learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches. Competing Interests: The authors declare that there are no conflicts of interest related to this article. (© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)
Databáze:	MEDLINE
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