Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study

Rodner E, Bocklitz T, Von Eggeling F, Ernst G, Chernavskaia O, Popp J, Denzler J, Guntinas-Lichius O (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Head and Neck-Journal For the Sciences and Specialties of the Head and Neck Wiley-Blackwell

Book Volume: 41

Pages Range: 116-121

Journal Issue: 1

DOI: 10.1002/hed.25489

Abstract

Background: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. Methods: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. Results: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. Conclusion: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.

Involved external institutions

Friedrich-Schiller-Universität Jena DE Germany (DE) Universitätsklinikum Jena DE Germany (DE) Leibniz-Institut für Photonische Technologien e.V. DE Germany (DE)

How to cite

APA:

Rodner, E., Bocklitz, T., Von Eggeling, F., Ernst, G., Chernavskaia, O., Popp, J.,... Guntinas-Lichius, O. (2019). Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study. Head and Neck-Journal For the Sciences and Specialties of the Head and Neck, 41(1), 116-121. https://doi.org/10.1002/hed.25489

MLA:

Rodner, Erik, et al. "Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study." Head and Neck-Journal For the Sciences and Specialties of the Head and Neck 41.1 (2019): 116-121.

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