Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images

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...

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Vydané v:Scientific reports Ročník 7; číslo 1; s. 3064 - 12
Hlavní autori: Lundström, Elin, Strand, Robin, Forslund, Anders, Bergsten, Peter, Weghuber, Daniel, Ahlström, Håkan, Kullberg, Joel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 08.06.2017
Nature Publishing Group
Nature Portfolio
Predmet:
59/57
631/443/319/1642/137/773
631/443/319/1642/393
639/705/794
Adipose tissue
Adipose tissue (brown)
Adipose Tissue, Brown - diagnostic imaging
Adolescent
Adult
Automation
Body Fat Distribution
Child
Clavicle
Computerized Image Processing
Correlation coefficient
Datoriserad bildbehandling
Female
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
multidisciplinary
NMR
Nuclear magnetic resonance
Science
Science (multidisciplinary)
Segmentation
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí: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 R 2 * 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, R 2 * 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, R 2 * 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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-01586-7