Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network.

Journal: Medical physics
Published Date: Jun 7, 2019

Abstract

PURPOSE: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution.

Authors

  • Ming Ma
    Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305-5847, USA.
  • Mark K Buyyounouski
    Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305-5847, USA.
  • Varun Vasudevan
    Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305-5847, USA.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.

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