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Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study

BACKGROUND: 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 semiauto...

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Autores principales: Kjønigsen, Lisa Jannicke, Harneshaug, Magnus, Fløtten, Ann-Monica, Karterud, Lena Korsmo, Petterson, Kent, Skjolde, Grethe, Eggesbø, Heidi B., Weedon-Fekjær, Harald, Henriksen, Hege Berg, Lauritzen, Peter M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820626/
https://www.ncbi.nlm.nih.gov/pubmed/31664547
http://dx.doi.org/10.1186/s41747-019-0122-5
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author Kjønigsen, Lisa Jannicke
Harneshaug, Magnus
Fløtten, Ann-Monica
Karterud, Lena Korsmo
Petterson, Kent
Skjolde, Grethe
Eggesbø, Heidi B.
Weedon-Fekjær, Harald
Henriksen, Hege Berg
Lauritzen, Peter M.
author_facet Kjønigsen, Lisa Jannicke
Harneshaug, Magnus
Fløtten, Ann-Monica
Karterud, Lena Korsmo
Petterson, Kent
Skjolde, Grethe
Eggesbø, Heidi B.
Weedon-Fekjær, Harald
Henriksen, Hege Berg
Lauritzen, Peter M.
author_sort Kjønigsen, Lisa Jannicke
collection PubMed
description BACKGROUND: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41747-019-0122-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-68206262019-11-14 Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study Kjønigsen, Lisa Jannicke Harneshaug, Magnus Fløtten, Ann-Monica Karterud, Lena Korsmo Petterson, Kent Skjolde, Grethe Eggesbø, Heidi B. Weedon-Fekjær, Harald Henriksen, Hege Berg Lauritzen, Peter M. Eur Radiol Exp Original Article BACKGROUND: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41747-019-0122-5) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-10-30 /pmc/articles/PMC6820626/ /pubmed/31664547 http://dx.doi.org/10.1186/s41747-019-0122-5 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.
spellingShingle Original Article
Kjønigsen, Lisa Jannicke
Harneshaug, Magnus
Fløtten, Ann-Monica
Karterud, Lena Korsmo
Petterson, Kent
Skjolde, Grethe
Eggesbø, Heidi B.
Weedon-Fekjær, Harald
Henriksen, Hege Berg
Lauritzen, Peter M.
Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title_full Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title_fullStr Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title_full_unstemmed Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title_short Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
title_sort reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820626/
https://www.ncbi.nlm.nih.gov/pubmed/31664547
http://dx.doi.org/10.1186/s41747-019-0122-5
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