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  5. Automatic classification and image analysis of confocal laser endomicroscopy images

Automatic classification and image analysis of confocal laser endomicroscopy images

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Automatic classification and image analysis of confocal laser endomicroscopy images

Automatic classification and image analysis of confocal laser endomicroscopy images

(Own Funds)

Overall project:
Project leader: Marc Aubreville, Andreas Maier
Project members: Nicolai Oetter, Florian Stelzle
Start date: October 1, 2014
End date:
Acronym:
Funding source:
URL:

Abstract

The goal of this project is to detect cancerous tissue in confocal lasermicroendoscopy (CLE) images of the oral cavity and the vocal cord. The current treatment of these diseases is a histological analysis of specimen and a surgical resection, which has a rather high long-term survival rate, or radiation therapy with a lower survival rate. An early detection of cancerous tissue could lead to a lowered complication rate for further treatment, as well as a better overall prognosis for patients. Further, an in-vivo diagnosis during operation could narrow down the area for the necessary surgical excision, which is especially beneficial for cancer of the vocal cords.

For this reason, we are applying methods of pattern recognition to facilitate and support diagnosis. We were able to show that these can be applied with high accuracies on CLE images.

Publications

  • Aubreville M., Knipfer C., Oetter N., Jaremenko C., Rodner E., Denzler J., Bohr C., Neumann H., Stelzle F., Maier A.:
    Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
    In: Scientific Reports 7 (2017), p. s41598-017
    ISSN: 2045-2322
    DOI: 10.1038/s41598-017-12320-8
    URL: https://www.nature.com/articles/s41598-017-12320-8.pdf
    BibTeX: Download
  • Aubreville M., Goncalves M., Knipfer C., Oetter N., Würfl T., Neumann H., Stelzle F., Bohr C., Maier A.:
    Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
    11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 (Funchal, January 19, 2018 - January 21, 2018)
    In: Sergi Bermúdez i Badia, Alberto Cliquet, Sheldon Wiebe, Reyer Zwiggelaar, Paul Anderson, Ana Fred, Hugo Gamboa, Giovanni Saggio (ed.): Communications in Computer and Information Science 2019
    DOI: 10.1007/978-3-030-29196-9_4
    BibTeX: Download
  • Stöve M., Aubreville M., Oetter N., Knipfer C., Neumann H., Stelzle F., Maier A.:
    Motion Artifact Detection in Confocal Laser Endomicroscopy Images
    Bildverarbeitung für die Medizin 2018 (Erlangen, Germany, March 11, 2018 - March 13, 2018)
    In: Bildverarbeitung für die Medizin 2018. Informatik aktuell., Berlin, Heidelberg: 2018
    DOI: 10.1007/978-3-662-56537-7_85
    URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Stoeve18-MAD.pdf
    BibTeX: Download
  • Aubreville M., Stöve M., Oetter N., Goncalves M., Knipfer C., Neumann H., Bohr C., Stelzle F., Maier A.:
    Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images.
    In: International Journal of Computer Assisted Radiology and Surgery 14 (2019), p. 31-42
    ISSN: 1861-6410
    DOI: 10.1007/s11548-018-1836-1
    BibTeX: Download
  • Goncalves M., Aubreville M., Mueller SK., Sievert M., Maier A., Iro H., Bohr C.:
    Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds.
    In: Acta Otorhinolaryngologica Italica (2019)
    ISSN: 0392-100X
    DOI: 10.14639/0392-100X-2121
    BibTeX: Download
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