Fully automated identification of skin morphology in raster-scan optoacoustic mesoscopy using artificial intelligence.

Journal: Medical physics
Published Date:

Abstract

PURPOSE: Identification of morphological characteristics of skin lesions is of vital importance in diagnosing diseases with dermatological manifestations. This task is often performed manually or in an automated way based on intensity level. Recently, ultra-broadband raster-scan optoacoustic mesoscopy (UWB-RSOM) was developed to offer unique cross-sectional optical imaging of the skin. A machine learning (ML) approach is proposed here to enable, for the first time, automated identification of skin layers in UWB-RSOM data.

Authors

  • Serafeim Moustakidis
    AIDEAS OÜ, Narva mnt 5, Tallinn, Harju maakond, 10117, Estonia.
  • Murad Omar
    Technische Universität München and Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, D-85764, Neuherberg, Germany.
  • Juan Aguirre
    Departamento de Tecnología Electrónica y de las Comunicaciones, Universidad Autónoma de Madrid, Madrid, Spain.
  • Pouyan Mohajerani
  • Vasilis Ntziachristos
    Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.