A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
PMID: 38922721

Abstract

OBJECTIVE: Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.

Authors

  • Manish Sahu
    Zuse Institute Berlin, Berlin, Germany. sahu@zib.de.
  • Yuliang Xiao
    Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
  • Jose L Porras
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Ameen Amanian
    Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada.
  • Aseem Jain
    Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
  • Andrew Thamboo
    Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada.
  • 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.
  • Masaru Ishii
    Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

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