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In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods

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In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods. / Eggert, Dennis; Bengs, Marcel; Westermann, Stephan; Gessert, Nils; Gerstner, Andreas O H; Mueller, Nina A; Bewarder, Julian; Schlaefer, Alexander; Betz, Christian; Laffers, Wiebke.

in: J BIOPHOTONICS, Jahrgang 15, Nr. 3, e202100167, 03.2022.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Eggert, D, Bengs, M, Westermann, S, Gessert, N, Gerstner, AOH, Mueller, NA, Bewarder, J, Schlaefer, A, Betz, C & Laffers, W 2022, 'In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods', J BIOPHOTONICS, Jg. 15, Nr. 3, e202100167. https://doi.org/10.1002/jbio.202100167

APA

Eggert, D., Bengs, M., Westermann, S., Gessert, N., Gerstner, A. O. H., Mueller, N. A., Bewarder, J., Schlaefer, A., Betz, C., & Laffers, W. (2022). In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods. J BIOPHOTONICS, 15(3), [e202100167]. https://doi.org/10.1002/jbio.202100167

Vancouver

Eggert D, Bengs M, Westermann S, Gessert N, Gerstner AOH, Mueller NA et al. In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods. J BIOPHOTONICS. 2022 Mär;15(3). e202100167. https://doi.org/10.1002/jbio.202100167

Bibtex

@article{1fbbd95e41564f1f9ce3476cc96e2e5f,
title = "In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods",
abstract = "Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81%, a sensitivity of 83% and a specificity of 79%.",
author = "Dennis Eggert and Marcel Bengs and Stephan Westermann and Nils Gessert and Gerstner, {Andreas O H} and Mueller, {Nina A} and Julian Bewarder and Alexander Schlaefer and Christian Betz and Wiebke Laffers",
note = "{\textcopyright} 2021 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.",
year = "2022",
month = mar,
doi = "10.1002/jbio.202100167",
language = "English",
volume = "15",
journal = "J BIOPHOTONICS",
issn = "1864-063X",
publisher = "Wiley-VCH Verlag GmbH",
number = "3",

}

RIS

TY - JOUR

T1 - In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods

AU - Eggert, Dennis

AU - Bengs, Marcel

AU - Westermann, Stephan

AU - Gessert, Nils

AU - Gerstner, Andreas O H

AU - Mueller, Nina A

AU - Bewarder, Julian

AU - Schlaefer, Alexander

AU - Betz, Christian

AU - Laffers, Wiebke

N1 - © 2021 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.

PY - 2022/3

Y1 - 2022/3

N2 - Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81%, a sensitivity of 83% and a specificity of 79%.

AB - Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81%, a sensitivity of 83% and a specificity of 79%.

U2 - 10.1002/jbio.202100167

DO - 10.1002/jbio.202100167

M3 - SCORING: Journal article

C2 - 34889065

VL - 15

JO - J BIOPHOTONICS

JF - J BIOPHOTONICS

SN - 1864-063X

IS - 3

M1 - e202100167

ER -