Trial reportPloS one2018
Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Semi-automatic muscle segmentation in MR images using deep registration-based label propagation.Pattern recognition · 2023Article
- Single slice thigh CT muscle group segmentation with domain adaptation and self-training.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.Magnetic resonance in medicine · 2023Article
- CMC-Net: 3D calf muscle compartment segmentation with sparse annotation.Medical image analysis · 2022Article
- Reproducibility of manual segmentation in muscle imaging.Acta myologica : myopathies and cardiomyopathies : official journal of the Mediterranean Society of Myology · 2021Article
- Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI.Diagnostics (Basel, Switzerland) · 2021Article
- Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI.Insights into imaging · 2020Article
- Associations of thigh muscle fat infiltration with isometric strength measurements based on chemical shift encoding-based water-fat magnetic resonance imaging.European radiology experimental · 2019Article
- Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy.Muscle & nerve · 2019Review
- Quantitative MRI Musculoskeletal Techniques: An Update.AJR. American journal of roentgenology · 2019Review
- Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine.BMC musculoskeletal disorders · 2019Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.