Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Journal: Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
Published Date: Oct 14, 2022

Abstract

OBJECTIVE: Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning models for automatic segmentation of postoperative vestibular schwannomas on gadolinium-enhanced T1-weighted magnetic resonance imaging (GdT1WI) and noncontrast high-resolution T2-weighted magnetic resonance imaging (HRT2WI).

Authors

  • Peter Yao
    Weill Cornell Medical College, Weill Cornell Medicine.
  • Sagit Stern Shavit
    Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine.
  • James Shin
    Department of Radiology, Weill Cornell Medicine, New York, New York.
  • Samuel Selesnick
    Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine.
  • C Douglas Phillips
    Department of Radiology, Weill Cornell Medicine, New York, New York.
  • Sara B Strauss
    Department of Radiology, Weill Cornell Medicine, New York, New York.

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