MRI pulse sequence integration for deep-learning-based brain metastases segmentation.

Journal: Medical physics
Published Date: Aug 18, 2021

Abstract

PURPOSE: Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep-learning methods have been applied to segmentation tasks in medical images, with promising results for computer-aided diagnosis. For MR images, effectively integrating different pulse sequences is important to optimize performance. However, the best way to integrate different pulse sequences remains unclear. In addition, networks trained with a certain subset of pulse sequences as input are unable to perform when given a subset of those pulse sequences. In this study, we evaluate multiple architectural features and characterize their effects in the task of metastasis segmentation while creating a method to robustly train a network to be able to work given any strict subset of the pulse sequences available during training.

Authors

  • Darvin Yi
    Stanford University, Department of Radiology, Stanford, CA.
  • Endre Grøvik
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Elizabeth Tong
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Michael Iv
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Kyrre Eeg Emblem
    Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway.
  • Line Brennhaug Nilsen
    Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway.
  • Cathrine Saxhaug
    Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Anna Latysheva
    Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Kari Dolven Jacobsen
    Department of Oncology, Oslo University Hospital, Oslo, Norway.
  • Åslaug Helland
    Department of Oncology, Oslo University Hospital, Oslo, Norway.
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.
  • Daniel Rubin
    Department of Radiology, Stanford University, Stanford, CA, USA.

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