Deep Learning Segmentation, Visualization, and Automated 3D Assessment of Ciliary Body in 3D Ultrasound Biomicroscopy Images.

Journal: Translational vision science & technology
PMID: 36180029

Abstract

PURPOSE: This study aimed to develop a fully automated deep learning ciliary body segmentation and assessment approach in three-dimensional ultrasound biomicroscopy (3D-UBM) images.

Authors

  • Ahmed Tahseen Minhaz
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
  • Duriye Damla Sevgi
    The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio.
  • Sunwoo Kwak
    Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA.
  • Alvin Kim
    Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, USA.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Richard W Helms
    UH CMC Division of Pediatric Ophthalmology and Adult Strabismus, Rainbow Babies and Children's Hospital, Cleveland, OH, USA.
  • Mahdi Bayat
    Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States of America.
  • David L Wilson
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106 and Department of Radiology, Case Western Reserve University, Cleveland, Ohio 44106.
  • Faruk H Orge
    UH CMC Division of Pediatric Ophthalmology and Adult Strabismus, Rainbow Babies and Children's Hospital, Cleveland, OH, USA.

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