ArticleMagma (New York, N.Y.)2016
A method for the automatic segmentation of brown adipose tissue.
Article in Magma (New York, N.Y.), 2016. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Brown Adipose Tissue: Molecular Mechanisms of Regulation, Physiological Functions, and Therapeutic Targets.MedComm · 2026Review
- Evaluation of high-fat diet-induced metabolic impairments in iBAT lipid metabolism usingMagma (New York, N.Y.) · 2026Article
- Imaging Brown Adipose Tissue: Current State and Future Perspective.Theranostics · 2025Review
- Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.Magma (New York, N.Y.) · 2023Article
- Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.Quantitative imaging in medicine and surgery · 2023Article
- Magnetic Resonance Imaging Techniques for Brown Adipose Tissue Detection.Frontiers in endocrinology · 2020Review
- Mapping brown adipose tissue based on fat water fraction provided by Z-spectral imaging.Journal of magnetic resonance imaging : JMRI · 2018Article
- Water-fat magnetic resonance imaging quantifies relative proportions of brown and white adipose tissues:Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Non-invasive Imaging Methods for Brown Adipose Tissue Detection and Function Evaluation.Internal medicine: open access · 2018Article
- Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images.Scientific reports · 2017Article
- Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans.IEEE transactions on medical imaging · 2017Article
- Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures.Magma (New York, N.Y.) · 2016Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBrown adipose tissue (BAT) plays a key role for thermogenesis in mammals and infants. Recent confirmation of BAT presence in adult humans has aroused great interest for its potential to initiate weight-loss and normalize metabolic disorders in diabetes and obesity. Reliable detection and differentiation of BAT from the surrounding white adipose tissue (WAT) and muscle is critical for assessment/quantification of BAT volume. This study evaluates magnetic resonance (MR) acquisition for BAT and the efficacy of different automated methods for MR features-based BAT segmentation to identify the best suitable method. MATERIALS AND
methodsMulti-point Dixon and multi-echo T2 spin-echo images were acquired from 12 mice using an Agilent 9.4T scanner. Four segmentation methods: multidimensional thresholding (MTh); region-growing (RG); fuzzy c-means (FCM) and neural-network (NNet) were evaluated for the interscapular region and validated against manually defined BAT, WAT and muscle.
resultsStatistical analysis of BAT segmentation yielded a median Dice-Statistical-Index, and sensitivity of 89.92% for NNet, 82.86% for FCM, 72.74% for RG, and 72.70%, for MTh, respectively.
conclusionThis study demonstrates that NNet improves the specificity to BAT from surrounding tissue based on 3-point Dixon and T2 MRI. This method facilitates quantification and longitudinal measurement of BAT in preclinical-models and human subjects.
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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.