Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning.

Journal: Medical physics
PMID: 39140650

Abstract

BACKGROUND: Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient-procedure-specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures.

Authors

  • Wilbur Ks Fum
    Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Mohammad Nazri Md Shah
    Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
  • Raja Rizal Azman Raja Aman
    Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
  • Khairul Azmi Abd Kadir
    Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
  • Sum Leong
    Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore.
  • Li Kuo Tan
    Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia.

Keywords

External Resources

Popular Topics
Recent Journals