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Proceedings Paper

Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
Author(s): Marcel Bengs; Stephan Westermann; Nils Gessert; Dennis Eggert; Andreas O. H. Gerstner; Nina A. Mueller; Christian Betz; Wiebke Laffers; Alexander Schlaefer
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Paper Abstract

Early detection of head and neck tumors is crucial for patient survival. Often, diagnoses are made based on endoscopic examination of the larynx followed by biopsy and histological analysis, leading to a high interobserver variability due to subjective assessment. In this regard, early non-invasive diagnostics independent of the clinician would be a valuable tool. A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue. However, HSI data processing is challenging due to high spectral variations, various image interferences, and the high dimensionality of the data. Therefore, performance of automatic HSI analysis has been limited and so far, mostly ex-vivo studies have been presented with deep learning. In this work, we analyze deep learning techniques for in-vivo hyperspectral laryngeal cancer detection. For this purpose we design and evaluate convolutional neural networks (CNNs) with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. For evaluation, we use an in-vivo data set with HSI of the oral cavity or oropharynx. Overall, we present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI and we show that jointly learning from the spatial and spectral domain improves classification accuracy notably. Our 3D spatio-spectral Densenet achieves an average accuracy of 81%.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141L (16 March 2020); doi: 10.1117/12.2549251
Show Author Affiliations
Marcel Bengs, Technische Univ. Hamburg-Harburg (Germany)
Stephan Westermann, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Nils Gessert, Technische Univ. Hamburg-Harburg (Germany)
Dennis Eggert, Universitätsklinikum Hamburg-Eppendorf (Germany)
Andreas O. H. Gerstner, Klinikum Braunschweig GmbH (Germany)
Nina A. Mueller, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Christian Betz, Universitätsklinikum Hamburg-Eppendorf (Germany)
Wiebke Laffers, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Universitätsklinikum Hamburg-Eppendorf (Germany)
Alexander Schlaefer, Technische Univ. Hamburg-Harburg (Germany)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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