Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Jul 13, 2019

Abstract

BACKGROUND: Diffusion-weighted imaging (DWI) in MRI plays an increasingly important role in diagnostic applications and developing imaging biomarkers. Automated whole-breast segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed on dynamic contrast-enhanced (DCE) MRI, automatic whole-breast segmentation in breast DWI MRI is still underdeveloped.

Authors

  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Aly A Mohamed
    Department of Radiology, University of Pittsburgh School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
  • Ruimei Chai
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Yuan Guo
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Bingjie Zheng
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Shandong Wu
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.

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