Advertisement

Motion Artifact Detection in Confocal Laser Endomicroscopy Images

  • Maike Stoeve
  • Marc Aubreville
  • Nicolai Oetter
  • Christian Knipfer
  • Helmut Neumann
  • Florian Stelzle
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)- cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity. Recently, the feasibility of automatic carcinoma detection for CLE images of sufficient quality was shown. However, in real world data sets a high amount of CLE images is corrupted by artifacts. Amongst the most prevalent artifact types are motion-induced image deteriorations. In the scope of this work, algorithmic approaches for the automatic detection of motion artifact-tainted image regions were developed. Hence, this work provides an important step towards clinical applicability of automatic carcinoma detection. Both, conventional machine learning and novel, deep learning-based approaches were assessed. The deep learning-based approach outperforms the conventional approaches, attaining an AUC of 0.90.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Blaszczak W, Barczak W, Wegner A, et al. Clinical value of monoclonal antibodies and tyrosine kinase inhibitors in the treatment of head and neck squamous cell carcinoma. Med Oncol. 2017;34(4):60.Google Scholar
  2. 2.
    Oetter N, Knipfer C, Rohde M, et al. Development and validation of a classification and scoring system for the diagnosis of oral squamous cell carcinomas through confocal laser endomicroscopy. J Transl Med. 2016;14(1):159.Google Scholar
  3. 3.
    Nathan CAO, Kaskas NM, Ma X, et al. Confocal laser endomicroscopy in the detection of head and neck precancerous lesions. J Otolaryngol Head Neck Surg. 2014;151(1):73–80.Google Scholar
  4. 4.
    Thong PS, Tandjung SS, Movania MM, et al. Toward real-time virtual biopsy of oral lesions using confocal laser endomicroscopy interfaced with embedded computing. J Biomed Opt. 2012;17(5):0560091–05600910.Google Scholar
  5. 5.
    Neumann H, Vieth M, Atreya R, et al. Prospective evaluation of the learning curve of confocal laser endomicroscopy in patients with IBD. Histol Histopathol. 2011;26(7):867.Google Scholar
  6. 6.
    Aubreville M, Knipfer C, Oetter N, et al. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep. 2017;20(7):11979.Google Scholar
  7. 7.
    Sharman M, Bacci B, Whittem T, et al. In vivo confocal endomicroscopy of small intestinal mucosal morphology in dogs. J Vet Intern Med. 2013;27(6):1372–1378.Google Scholar
  8. 8.
    Jaremenko C, Maier A, Steidl S, et al. Classification of confocal laser endomicroscopic images of the oral cavity to distinguish pathological from healthy tissue. Proc BVM. 2015; p. 479–485.Google Scholar
  9. 9.
    Dalal N, Triggs B, et al.; IEEE. Histograms of oriented gradients for human detection. Proc CVPR. 2005;1:886–893.Google Scholar
  10. 10.
    Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. Proc VCPR. 2016; p. 2818–2826.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Maike Stoeve
    • 1
  • Marc Aubreville
    • 1
  • Nicolai Oetter
    • 2
    • 3
  • Christian Knipfer
    • 4
    • 3
  • Helmut Neumann
    • 5
    • 3
  • Florian Stelzle
    • 2
    • 3
  • Andreas Maier
    • 1
    • 3
  1. 1.Pattern Recognition Lab, Computer ScienceFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Department of Oral and Maxillofacial SurgeryUniversity Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  3. 3.Erlangen Graduate School in Advanced Optical Technologies (SAOT)Friedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  4. 4.Department of Oral and Maxillofacial SurgeryUniversity Medical Center Hamburg-EppendorfHamburgDeutschland
  5. 5.First Department of Internal MedicineUniversity Hospital Mainz, Johannes Gutenberg-Universität MainzMainzDeutschland

Personalised recommendations