Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method.

Journal: Academic radiology
Published Date: May 1, 2020

Abstract

BACKGROUND: The purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative setting for margin assessment.

Authors

  • Diana Mojahed
    Department of Biomedical Engineering, Columbia University, New York, New York; Department of Electrical Engineering, Columbia University, New York, New York.
  • Richard S Ha
    Department of Radiology, Columbia University Medical Center, 622 W 168th St, PB-1-301, New York, New York 10032. Electronic address: rh2616@columbia.edu.
  • Peter Chang
    Department of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Yu Gan
    Biomedical Engineering Department, Stevens Institute of Technology, Hoboken, NJ 07030 USA.
  • Xinwen Yao
    Department of Electrical Engineering, Columbia University, New York, New York.
  • Brigid Angelini
    Department of Electrical Engineering, Columbia University, New York, New York.
  • Hanina Hibshoosh
    Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York.
  • Bret Taback
    Department of Surgery, Columbia University Medical Center, New York, NY, USA.
  • Christine P Hendon
    Department of Electrical Engineering, Columbia University, New York, New York.

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