A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
PMID: 36883992

Abstract

OBJECTIVE: Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot-assisted procedures in this space. This study evaluates a state-of-the-art deep learning pipeline for semantic segmentation of temporal bone anatomy.

Authors

  • Andy S Ding
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine.
  • Alexander Lu
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Zhaoshuo Li
    Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland.
  • Manish Sahu
    Zuse Institute Berlin, Berlin, Germany. sahu@zib.de.
  • Deepa Galaiya
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Jeffrey H Siewerdsen
    Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.
  • Russell H Taylor
    Johns Hopkins University, Baltimore, MD, USA.
  • Francis X Creighton
    Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.

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