Hippocampus segmentation on noncontrast CT using deep learning.

Journal: Medical physics
Published Date: Jun 2, 2020

Abstract

PURPOSE: Accurate segmentation of the hippocampus for hippocampal avoidance whole-brain radiotherapy currently requires high-resolution magnetic resonance imaging (MRI) in addition to neuroanatomic expertise for manual segmentation. Removing the need for MR images to identify the hippocampus would reduce planning complexity, the need for a treatment planning MR imaging session, potential uncertainties associated with MRI-computed tomography (CT) image registration, and cost. Three-dimensional (3D) deep convolutional network models have the potential to automate hippocampal segmentation. In this study, we investigate the accuracy and reliability of hippocampal segmentation by automated deep learning models from CT alone and compare the accuracy to experts using MRI fusion.

Authors

  • Evan Porter
    Department of Medical Physics, Wayne State University, Detroit, MI, USA.
  • Patricia Fuentes
    Beaumont Artificial Intelligence Research Laboratory, Beaumont Health Systems, Royal Oak, MI, USA.
  • Zaid Siddiqui
    Beaumont Artificial Intelligence Research Laboratory, Beaumont Health Systems, Royal Oak, MI, USA.
  • Andrew Thompson
    Beaumont Artificial Intelligence Research Laboratory, Beaumont Health Systems, Royal Oak, MI, USA.
  • Ronald Levitin
    Beaumont Artificial Intelligence Research Laboratory, Beaumont Health Systems, Royal Oak, MI, USA.
  • David Solis
    Beaumont Artificial Intelligence Research Laboratory, Beaumont Health Systems, Royal Oak, MI, USA.
  • Nick Myziuk
    Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA.
  • Thomas Guerrero
    Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States.

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