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Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: The reference database MyoSegmenTUM spine

  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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.

Original languageEnglish
Article number152
JournalBMC Musculoskeletal Disorders
Volume20
Issue number1
DOIs
StatePublished - 9 Apr 2019

Keywords

  • Bone marrow
  • Lumbar spine
  • Magnetic resonance imaging
  • Muscle
  • Proton density fat fraction
  • Segmentation

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