Automated labeling using tracked ultrasound imaging: Application in tracking vertebrae during spine surgery.

Journal: Medical image analysis
Published Date: May 31, 2025

Abstract

PURPOSE: Recent advancements in machine learning (ML) allow for rapid analysis of complex image data, which supports the use of ultrasound (US)-based solutions in interventional procedures. These solutions often require large, labeled datasets that can be time-consuming to curate and subject to inter- and intra-labeler variability. This work presents a practical method for automated labeling of US images by transferring labels from 3D diagnostic images (e.g., CT or MR) using tracked US imaging to support supervised training. The approach was applied to segmenting spinal vertebrae, and the quality of the generated labels was evaluated by registering individual vertebrae from US to CT images to account for potential spinal deformation during surgery.

Authors

  • Debarghya China
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
  • Luke J MacLean
    Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
  • Jinchi Wei
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • Nicholas Theodore
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Orthopaedic Surgery & Biomedical Engineering, Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: theodore@jhmi.edu.
  • Norbert Johnson
    Globus Medical, Audubon, PA, United States.
  • Neil Crawford
    Globus Medical, Audubon, PA, United States.
  • Kai Ding
    Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, College of Public Health.
  • Ali Uneri
    Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.

Keywords

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