Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

Journal: Academic radiology
PMID: 31326311

Abstract

RATIONALE AND OBJECTIVES: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia.

Authors

  • Ryan Barnard
    Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Josh Tan
    Department of Radiology, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157.
  • Brandon Roller
    Department of Radiology, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157.
  • Caroline Chiles
    Department of Radiology, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157.
  • Ashley A Weaver
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Robert D Boutin
    Department of Radiology, University of California, Davis, School of Medicine, Sacramento, California.
  • Stephen B Kritchevsky
    Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Leon Lenchik
    Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina.

Keywords

External Resources

Popular Topics
Recent Journals