A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

Journal: Medical physics
Published Date: Apr 4, 2019

Abstract

PURPOSE: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk.

Authors

  • Jason W Chan
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Vasant Kearney
    Department of Radiation Oncology, University of California, San Francisco, CA, United States of America.
  • Samuel Haaf
  • Susan Wu
    Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
  • Madeleine Bogdanov
    Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
  • Mariah Reddick
    Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
  • Nayha Dixit
    Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
  • Atchar Sudhyadhom
  • Josephine Chen
    Biomedical Informatics Training Program, Stanford University, Stanford, CA.
  • Sue S Yom
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Timothy D Solberg
    U.S. Food and Drug Administration, Silver Spring, Maryland.

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