Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment.

Journal: Clinical radiology
PMID: 35260232

Abstract

AIM: To develop a fully automated deep-learning-based approach to measure muscle area for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen without any case exclusion criteria, for opportunistic screening for frailty.

Authors

  • S Islam
  • F Kanavati
    Comprehensive Cancer Imaging Centre, Division of Cancer, Dept of Cancer and Surgery, Faculty of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NS, UK.
  • Z Arain
    Comprehensive Cancer Imaging Centre, Division of Cancer, Dept of Cancer and Surgery, Faculty of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NS, UK.
  • O Fadeeva Da Costa
    Comprehensive Cancer Imaging Centre, Division of Cancer, Dept of Cancer and Surgery, Faculty of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NS, UK; Dept of Radiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Rd, London, W12 0NS, UK.
  • W Crum
    Comprehensive Cancer Imaging Centre, Division of Cancer, Dept of Cancer and Surgery, Faculty of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NS, UK.
  • E O Aboagye
    Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London W12 0NN, UK.
  • A G Rockall
    Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London W12 0NN, UK; Department of Radiology Hammersmith Hospital, Imperial College Healthcare NHS Trust, Du Cane Road, London W12 0NN, UK.

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