A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

Journal: International journal of computer assisted radiology and surgery
Published Date: Mar 6, 2019

Abstract

PURPOSE: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

Authors

  • Tatyana Ivanovska
    Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany. tiva@phys.uni-goettingen.de.
  • Thomas G Jentschke
    Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany.
  • Amro Daboul
    Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Fleischmannstr. 42-44, 17475, Greifswald, Germany.
  • Katrin Hegenscheid
    Unfallkrankenhaus Berlin, Warener Str. 7, 12683, Berlin, Germany.
  • Henry Völzke
    Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
  • Florentin Wörgötter
    Department of Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz, 1, 37077, Göttingen, Germany.

Keywords

External Resources

Popular Topics
Recent Journals