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Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0001-7764-1787
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.ORCID iD: 0000-0001-9109-4556
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, 3064Article in journal (Refereed) Published
Abstract [en]

Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a promising anti-obesity target. This work presents an automated method for segmenting cervical-supraclavicular adipose tissue for enabling time-efficient and objective measurements in large cohort research studies of BAT. Fat fraction (FF) and R2* maps were reconstructed from water-fat magnetic resonance imaging (MRI) of 25 subjects. A multi-atlas approach, based on atlases from nine subjects, was chosen as automated segmentation strategy. A semi-automated reference method was used to validate the automated method in the remaining subjects. Automated segmentations were obtained from a pipeline of preprocessing, affine registration, elastic registration and postprocessing. The automated method was validated with respect to segmentation overlap (Dice similarity coefficient, Dice) and estimations of FF, R2* and segmented volume. Bias in measurement results was also evaluated. Segmentation overlaps of Dice = 0.93 +/- 0.03 (mean +/- standard deviation) and correlation coefficients of r > 0.99 (P < 0.0001) in FF, R2* and volume estimates, between the methods, were observed. Dice and BMI were positively correlated (r = 0.54, P = 0.03) but no other significant bias was obtained (P >= 0.07). The automated method compared well with the reference method and can therefore be suitable for time-efficient and objective measurements in large cohort research studies of BAT.

Place, publisher, year, edition, pages
2017. Vol. 7, 3064
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-323968DOI: 10.1038/s41598-017-01586-7ISI: 000402865000003PubMedID: 28596551OAI: oai:DiVA.org:uu-323968DiVA: diva2:1108055
Funder
Swedish Research CouncilEU, FP7, Seventh Framework Programme, 279153
Available from: 2017-06-08 Created: 2017-06-12 Last updated: 2017-09-19Bibliographically approved

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Strand, RobinForslund, AndersBergsten, PeterAhlström, HåkanKullberg, Joel

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Lundström, ElinStrand, RobinForslund, AndersBergsten, PeterAhlström, HåkanKullberg, Joel
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RadiologyDivision of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionDepartment of Women's and Children's HealthDepartment of Medical Cell Biology
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