Tackling the class imbalance problem of deep learning-based head and neck organ segmentation.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 16, 2022

Abstract

PURPOSE: The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image- guided radiation therapy. The automation of the segmentation task is therefore of high clinical relevance. Deep learning (DL)-based medical image segmentation is currently the most successful approach, but suffers from the over-presence of the background class and the anatomically given organ size difference, which is most severe in the head and neck (HAN) area.

Authors

  • Elias Tappeiner
    Department of Biomedical Computer Science and Mechatronics, University for Health Sciences, Medical Informatics and Technology, 6060, Hall, Tyrol, Austria. elias.tappeiner@umit.at.
  • Martin Welk
    Department for Biomedical Computer Science and Mechatronics, UMIT-Private University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, 6060, Hall in Tyrol, Tyrol, Austria.
  • Rainer Schubert
    Department of Biomedical Computer Science and Mechatronics, University for Health Sciences, Medical Informatics and Technology, 6060, Hall, Tyrol, Austria.

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