Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.

Journal: Medical physics
Published Date: Oct 1, 2019

Abstract

PURPOSE: Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge-based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances.

Authors

  • Erik Verburg
    Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, 3584 CX, the Netherlands.
  • Jelmer M Wolterink
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • Stephanie N de Waard
    Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, 3584 CX, the Netherlands.
  • Ivana Išgum
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • Carla H van Gils
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, 3584 CX, the Netherlands.
  • Wouter B Veldhuis
    Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, 3584 CX, the Netherlands.
  • Kenneth G A Gilhuijs
    Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, 3584 CX, the Netherlands.

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