ArticleMagnetic resonance in medicine2017
3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle.
Article in Magnetic resonance in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Magnetic Resonance Imaging Biomarkers of Muscle.Tomography (Ann Arbor, Mich.) · 2024Review
- Application of MRI images based on Spatial Fuzzy Clustering Algorithm guided by Neuroendoscopy in the treatment of Tumors in the Saddle Region.Pakistan journal of medical sciences · 2021Article
- Role of the Extracellular Matrix in Loss of Muscle Force With Age and Unloading Using Magnetic Resonance Imaging, Biochemical Analysis, and Computational Models.Frontiers in physiology · 2020Review
- MRI adipose tissue and muscle composition analysis-a review of automation techniques.The British journal of radiology · 2018Review
- Advanced body composition assessment: from body mass index to body composition profiling.Journal of investigative medicine : the official publication of the American Federation for Clinical Research · 2018Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo develop and evaluate an automated algorithm to segment intramuscular adipose (IMAT) and connective (IMCT) tissue from musculoskeletal MRI images acquired with a dual echo Ultrashort TE (UTE) sequence. THEORY AND
methodsThe dual echo images and calculated structure tensor images are the inputs to the multichannel fuzzy cluster mean (MCFCM) algorithm. Modifications to the basic multichannel fuzzy cluster mean include an adaptive spatial term and bias shading correction. The algorithm was tested on digital phantoms simulating IMAT/IMCT tissue under varying conditions of image noise and bias and on ten subjects with varying amounts of IMAT/IMCT.
resultsThe MCFCM including the adaptive spatial term and bias shading correction performed better than the original MCFCM and adaptive spatial MCFCM algorithms. IMAT/IMCT was segmented from the unsmoothed simulated phantom data with a mean Dice coefficient of 0.933 ±0.001 when contrast-to-noise (CNR) was 140 and bias was varied between 30% and 65%. The algorithm yielded accurate in vivo segmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ±0.066.
conclusionThe proposed algorithm is completely automated and yielded accurate segmentation of intramuscular adipose and connective tissue in the digital phantom and in human calf data. Magn Reson Med 77:870-883, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Registered trials
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