ArticleBMC musculoskeletal disorders2019
Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine.
Article in BMC musculoskeletal disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Spine muscle auto segmentation techniques in MRI imaging: a systematic review.BMC musculoskeletal disorders · 2024Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Automated deep-learning quantification of intramuscular fat in lumbar spine muscles on Dixon MRI: validation and normative reference values from 173 healthy adults.BMC medical imaging · 2026Article
- DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Assessment of lumbar paraspinal muscle morphology using mDixon Quant magnetic resonance imaging (MRI): a cross-sectional study in healthy subjects.Quantitative imaging in medicine and surgery · 2024Article
- Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow.Zeitschrift fur medizinische Physik · 2024Article
- LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images.BMC musculoskeletal disorders · 2020Article
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Authors and funding
13 authors at 1 institution in 1 country.
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Abstract
backgroundMagnetic resonance imaging (MRI) is the modality of choice for diagnosing and monitoring muscular tissue pathologies and bone marrow alterations in the context of lower back pain, neuromuscular diseases and osteoporosis. Chemical shift encoding-based water-fat MRI allows for reliable determination of proton density fat fraction (PDFF) of the muscle and bone marrow. Prior to quantitative data extraction, segmentation of the examined structures is needed. Performed manually, the segmentation process is time consuming and therefore limiting the clinical applicability. Thus, the development of automated segmentation algorithms is an ongoing research focus. CONSTRUCTION AND CONTENT: This database provides ground truth data which may help to develop and test automatic lumbar muscle and vertebra segmentation algorithms. Lumbar muscle groups and vertebral bodies (L1 to L5) were manually segmented in chemical shift encoding-based water-fat MRI and made publically available in the database MyoSegmenTUM. The database consists of water, fat and PDFF images with corresponding segmentation masks for lumbar muscle groups (right/left erector spinae and psoas muscles, respectively) and lumbar vertebral bodies 1-5 of 54 healthy Caucasian subjects. The database is freely accessible online at https://osf.io/3j54b/?view_only=f5089274d4a449cda2fef1d2df0ecc56 .
conclusionA development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine.
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