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

Abstract 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...

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Autores principales:	Elin Lundström, Robin Strand, Anders Forslund, Peter Bergsten, Daniel Weghuber, Håkan Ahlström, Joel Kullberg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/83c0c9bd0288444a852d8c6b71e38e45
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spelling	oai:doaj.org-article:83c0c9bd0288444a852d8c6b71e38e452021-12-02T15:05:48ZAutomated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images10.1038/s41598-017-01586-72045-2322https://doaj.org/article/83c0c9bd0288444a852d8c6b71e38e452017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01586-7https://doaj.org/toc/2045-2322Abstract 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.Elin LundströmRobin StrandAnders ForslundPeter BergstenDaniel WeghuberHåkan AhlströmJoel KullbergNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elin Lundström Robin Strand Anders Forslund Peter Bergsten Daniel Weghuber Håkan Ahlström Joel Kullberg Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
description	Abstract 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.
format	article
author	Elin Lundström Robin Strand Anders Forslund Peter Bergsten Daniel Weghuber Håkan Ahlström Joel Kullberg
author_facet	Elin Lundström Robin Strand Anders Forslund Peter Bergsten Daniel Weghuber Håkan Ahlström Joel Kullberg
author_sort	Elin Lundström
title	Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
title_short	Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
title_full	Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
title_fullStr	Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
title_full_unstemmed	Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
title_sort	automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/83c0c9bd0288444a852d8c6b71e38e45
work_keys_str_mv	AT elinlundstrom automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT robinstrand automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT andersforslund automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT peterbergsten automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT danielweghuber automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT hakanahlstrom automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages AT joelkullberg automatedsegmentationofhumancervicalsupraclavicularadiposetissueinmagneticresonanceimages
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Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images

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