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dc.contributor.authorKjønigsen, Lisa Jannicke
dc.contributor.authorHarneshaug, Magnus
dc.contributor.authorFløtten, Ann-Monica
dc.contributor.authorKarterud, Lena Korsmo
dc.contributor.authorPetterson, Kent
dc.contributor.authorSkjolde, Grethe
dc.contributor.authorEggesbø, Heidi Beate
dc.contributor.authorWeedon-Fekjær, Harald
dc.contributor.authorHenriksen, Hege
dc.contributor.authorLauritzen, Peter Mæhre
dc.identifier.citationEuropean Radiology Experimental. 2019, 3:42 1-8.en_US
dc.description.abstractBackground: Segmentation of computed tomography (CT) images provides quantitative data on body tissue composition, which may greatly impact the development and progression of diseases such as type 2 diabetes mellitus and cancer. We aimed to evaluate the inter- and intraobserver variation of semiautomated segmentation, to assess whether multiple observers may interchangeably perform this task. Methods: Anonymised, unenhanced, single mid-abdominal CT images were acquired from 132 subjects from two previous studies. Semiautomated segmentation was performed using a proprietary software package. Abdominal muscle compartment (AMC), inter- and intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were identified according to pre-established attenuation ranges. The segmentation was performed by four observers: an oncology resident with extensive training and three radiographers with a 2-week training programme. To assess interobserver variation, segmentation of each CT image was performed individually by two or more observers. To assess intraobserver variation, three of the observers did repeated segmentations of the images. The distribution of variation between subjects, observers and random noise was estimated by a mixed effects model. Inter- and intraobserver correlation was assessed by intraclass correlation coefficient (ICC). Results: For all four tissue compartments, the observer variations were far lower than random noise by factors ranging from 1.6 to 3.6 and those between subjects by factors ranging from 7.3 to 186.1. All interobserver ICC was ≥ 0.938, and all intraobserver ICC was ≥ 0.996. Conclusions: Body composition segmentation showed a very low level of operator dependability. Multiple observers may interchangeably perform this task with highly reproducible results.en_US
dc.description.sponsorshipThe study received no external funding and was carried out with the resources of the author affiliated institutions.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectBody composition;en_US
dc.subjectAbdominal fat;en_US
dc.subjectSkeletal muscle;en_US
dc.subjectTomography (X-ray computed);en_US
dc.subjectObserver variation;en_US
dc.titleReproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver studyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.source.journalEuropean Radiology Experimentalen_US
cristin.unitnameAvd Alderspsykiatri

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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal