Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Dec 31, 2018

Abstract

PURPOSE: To design a deep learning algorithm that automatically delineates lung tumors seen on weekly magnetic resonance imaging (MRI) scans acquired during radiotherapy and facilitates the analysis of geometric tumor changes.

Authors

  • Chuang Wang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Neelam Tyagi
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Andreas Rimner
    Memorial Sloan Kettering Cancer Center, New York, New York.
  • Yu-Chi Hu
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Harini Veeraraghavan
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; veerarah@mskcc.org.
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Margie Hunt
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Gig Mageras
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.

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