Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine. View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Egon Burian, Alexander Rohrmeier, Sarah Schlaeger, Michael Dieckmeyer, Maximilian N Diefenbach, Jan Syväri, Elisabeth Klupp, Dominik Weidlich, Claus Zimmer, Ernst J Rummeny, Dimitrios C Karampinos, Jan S Kirschke, Thomas Baum

ABSTRACT

BACKGROUND: Magnetic 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 . CONCLUSION: A development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine. More... »

PAGES

152

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12891-019-2528-x

DOI

http://dx.doi.org/10.1186/s12891-019-2528-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113300172

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30961552


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39 schema:description BACKGROUND: Magnetic 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 . CONCLUSION: A development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine.
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