Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.

Journal: Medical physics
Published Date: Mar 12, 2019

Abstract

OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identification of the pectoral muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed texture-field orientation (TFO) method using conventional image feature analysis. Pectoral muscle segmentation is an important step for automated image analyses such as breast density or parenchymal pattern classification, lesion detection, and multiview correlation.

Authors

  • Xiangyuan Ma
    Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Jun Wei
    Guangzhou Perception Vision Medical Technology Inc. Guangzhou 510000 China.
  • Chuan Zhou
    Department of Radiology, The University of Michigan, Ann Arbor, MI, 48109, USA.
  • Mark A Helvie
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
  • Heang-Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Lubomir M Hadjiiski
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Yao Lu
    Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo First Hospital, Ningbo, China.

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