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Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images

  • Zihao Tang
  • Chenyu Wang
  • Phu Hoang
  • Sidong Liu
  • Weidong Cai
  • Domenic Soligo
  • Ruth Oliver
  • Michael Barnett
  • Ché Fornusek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

The quantification of muscle mass is important in clinical populations with chronic paralysis, cachexia, and sarcopenia. This is especially true when testing interventions which are designed to maintain or improve muscle mass. The purpose of this paper is to report on an automated method of MRI-based thigh muscle segmentation framework that minimizes longitudinal deviation by using femur segmentation as a reference in a two-phase registration. Imaging data from seven patients with severe multiple sclerosis who had undergone MRI scans at multiple time points were used to develop and validate our method. The proposed framework results in robust, automated co-registration between baseline and follow up scans, and generates a reliable thigh muscle mask that excludes intramuscular fat.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zihao Tang
    • 1
    • 2
  • Chenyu Wang
    • 1
    • 3
  • Phu Hoang
    • 4
  • Sidong Liu
    • 2
  • Weidong Cai
    • 2
  • Domenic Soligo
    • 5
  • Ruth Oliver
    • 1
  • Michael Barnett
    • 1
    • 3
  • Ché Fornusek
    • 6
  1. 1.Sydney Neuroimaging Analysis CentreSydneyAustralia
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia
  3. 3.Brain and Mind CentreUniversity of SydneySydneyAustralia
  4. 4.Neuroscience Research AustraliaUniversity of New South WalesSydneyAustralia
  5. 5.I-MED RadiologySydneyAustralia
  6. 6.Discipline of Exercise and Sport Science, Faculty of Medicine and HealthUniversity of SydneySydneyAustralia

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