Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.

Journal: Journal of cachexia, sarcopenia and muscle
PMID: 40022453

Abstract

BACKGROUND: Age-related conditions, such as osteoporosis and sarcopenia, alongside chronic diseases, can result in significant musculoskeletal tissue loss. This impacts individuals' quality of life and increases risk of falls and fractures. Computed tomography (CT) has been widely used for assessing musculoskeletal tissues. Although automatic techniques have been investigated for segmenting tissues in the abdomen and mid-thigh regions, studies in proximal hip remain limited. This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip.

Authors

  • Mahdi Imani
  • Jared Buratto
    Department of Medicine-Western Health, The University of Melbourne, St. Albans, Victoria, Australia.
  • Thang Dao
    Department of Medicine-Western Health, The University of Melbourne, St. Albans, Victoria, Australia.
  • Erik Meijering
    Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Sara Vogrin
    Department of Medicine, Melbourne Medical School, University of Melbourne, St. Albans, Australia.
  • Timothy C Y Kwok
    Jockey Club Centre for Osteoporosis Care and Control, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Eric S Orwoll
    Division of Endocrinology, Diabetes and Clinical Nutrition, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA.
  • Peggy M Cawthon
    Research Institute, California Pacific Medical Center, San Francisco, California, USA.
  • Gustavo Duque
    Bone, Muscle & Geroscience Group, Research Institute of the McGill University Health Centre, McGill University, 1001 Decarie Blvd, Room EM1.3226, Montreal, QC, H4A 3J1, Canada, 1 514 934 1934.

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