TY - JOUR
T1 - Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images
T2 - The reference database MyoSegmenTUM
AU - Schlaeger, Sarah
AU - Freitag, Friedemann
AU - Klupp, Elisabeth
AU - Dieckmeyer, Michael
AU - Weidlich, Dominik
AU - Inhuber, Stephanie
AU - Deschauer, Marcus
AU - Schoser, Benedikt
AU - Bublitz, Sarah
AU - Montagnese, Federica
AU - Zimmer, Claus
AU - Rummeny, Ernst J.
AU - Karampinos, Dimitrios C.
AU - Kirschke, Jan S.
AU - Baum, Thomas
N1 - Publisher Copyright:
© 2018 Schlaeger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/6
Y1 - 2018/6
N2 - Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.
AB - Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.
UR - http://www.scopus.com/inward/record.url?scp=85048196573&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0198200
DO - 10.1371/journal.pone.0198200
M3 - Article
C2 - 29879128
AN - SCOPUS:85048196573
SN - 1932-6203
VL - 13
JO - PLoS ONE
JF - PLoS ONE
IS - 6
M1 - e0198200
ER -