TY - GEN
T1 - Automatic segmentation of thigh muscle in longitudinal 3D T1-weighted magnetic resonance (MR) images
AU - Tang, Zihao
AU - Wang, Chenyu
AU - Hoang, Phu
AU - Liu, Sidong
AU - Cai, Weidong
AU - Soligo, Domenic
AU - Oliver, Ruth
AU - Barnett, Michael
AU - Fornusek, Ché
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85054813518&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00807-9_2
DO - 10.1007/978-3-030-00807-9_2
M3 - Conference proceeding contribution
AN - SCOPUS:85054813518
SN - 9783030008062
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 21
BT - Data driven treatment response assessment and preterm, perinatal, and paediatric image analysis
A2 - Melbourne, Andrew
A2 - Licandro, Roxane
A2 - DiFranco, Matthew
A2 - Rota, Paolo
A2 - Gau, Melanie
A2 - Kampel, Martin
A2 - Aughwane, Rosalind
A2 - Moeskops, Pim
A2 - Schwartz, Ernst
A2 - Robinson, Emma
A2 - Makropoulos, Antonios
PB - Springer-VDI-Verlag GmbH & Co. KG
T2 - 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -