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Published in:
2018 | OriginalPaper | Chapter
Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images
Authors : Zihao Tang, Chenyu Wang, Phu Hoang, Sidong Liu, Weidong Cai, Domenic Soligo, Ruth Oliver, Michael Barnett, Ché Fornusek
Published in: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis
Publisher: Springer International Publishing
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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.