In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (Conference Presentation)

Dennis Eggert; Marcel Bengs; Stephan Westermann; Nils Gessert; Andreas O. H. Gerstner; Nina A. Müller; Alexander Schlaefer; Christian Betz; Wiebke Laffers

doi:10.1117/12.2557496

6 March 2020 In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (Conference Presentation)

Dennis Eggert, Marcel Bengs, Stephan Westermann, Nils Gessert, Andreas O. H. Gerstner, Nina A. Müller, Alexander Schlaefer, Christian Betz, Wiebke Laffers

Author Affiliations +

Proceedings Volume 11213, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020; 112130L (2020) https://doi.org/10.1117/12.2557496
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Here we present a study where we used in vivo hyperspectral imaging (HSI) for the detection of upper aerodigestive tract (UADT) cancer. Hyperspectral datasets were recorded in 100 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using, different deep learning techniques. Our method is based on convolutional neural networks (CNNs) with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using both the spatial and spectral domain improves classification accuracy notably. Our 3D spatio-spectral Densenet classification method achieves an average accuracy of over 80%.

Conference Presentation

Citation Download Citation

Dennis Eggert, Marcel Bengs, Stephan Westermann, Nils Gessert, Andreas O. H. Gerstner, Nina A. Müller, Alexander Schlaefer, Christian Betz, and Wiebke Laffers "In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (Conference Presentation)", Proc. SPIE 11213, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020, 112130L (6 March 2020); https://doi.org/10.1117/12.2557496

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