Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Journal: Investigative ophthalmology & visual science
PMID: 38696188

Abstract

PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON.

Authors

  • Adham M Alkhadrawi
    Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Lisa Y Lin
    Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States.
  • Saul A Langarica
    Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Kyungsu Kim
    Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea. kskim.doc@gmail.com.
  • Sierra K Ha
    Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States.
  • Nahyoung G Lee
    Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, MA, USA.
  • Synho Do
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA. sdo@mgh.harvard.edu.

Keywords

External Resources

Popular Topics
Recent Journals