Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, Bohr C, Neumann H, Stelzle F, Maier A (2017)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2017

Journal

Book Volume: 7

Pages Range: s41598-017

Journal Issue: 1

URI: https://www.nature.com/articles/s41598-017-12320-8.pdf

DOI: 10.1038/s41598-017-12320-8

Open Access Link: https://www.nature.com/articles/s41598-017-12320-8.pdf

Abstract

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, su cient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 speci c locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, speci city 90%). 

Authors with CRIS profile

Related research project(s)

Involved external institutions

Friedrich-Schiller-Universität Jena Germany (DE) Johannes Gutenberg-Universität Mainz (JGU) Germany (DE)

How to cite

APA:

Aubreville, M., Knipfer, C., Oetter, N., Jaremenko, C., Rodner, E., Denzler, J.,... Maier, A. (2017). Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Scientific Reports, 7(1), s41598-017. https://doi.org/10.1038/s41598-017-12320-8

MLA:

Aubreville, Marc, et al. "Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning." Scientific Reports 7.1 (2017): s41598-017.

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