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Laryngeal lesion classification based on vascular patterns in contact endoscopy and narrow band imaging: Manual versus automatic approach

  • Nazila Esmaeili
  • , Alfredo Illanes
  • , Axel Boese
  • , Nikolaos Davaris
  • , Christoph Arens
  • , Nassir Navab
  • , Michael Friebe
  • Otto-von-Guericke University
  • Magdeburg University Hospital
  • IDTM GmbH

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.

Original languageEnglish
Article number4018
Pages (from-to)1-12
Number of pages12
JournalSensors (Switzerland)
Volume20
Issue number14
DOIs
StatePublished - 2 Jul 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Automatic classification
  • Contact endoscopy
  • Feature extraction
  • Laryngeal cancer
  • Machine learning
  • Narrow band imaging

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