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

Abstract 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...

Full description

Bibliographic Details
Main Authors: Lisa Jannicke Kjønigsen, Magnus Harneshaug, Ann-Monica Fløtten, Lena Korsmo Karterud, Kent Petterson, Grethe Skjolde, Heidi B. Eggesbø, Harald Weedon-Fekjær, Hege Berg Henriksen, Peter M. Lauritzen
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:European Radiology Experimental
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41747-019-0122-5
id doaj-9350d7833f904bef9ed03c723d869ef2
record_format Article
spelling doaj-9350d7833f904bef9ed03c723d869ef22020-11-25T03:06:10ZengSpringerOpenEuropean Radiology Experimental2509-92802019-10-01311810.1186/s41747-019-0122-5Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver studyLisa Jannicke Kjønigsen0Magnus Harneshaug1Ann-Monica Fløtten2Lena Korsmo Karterud3Kent Petterson4Grethe Skjolde5Heidi B. Eggesbø6Harald Weedon-Fekjær7Hege Berg Henriksen8Peter M. Lauritzen9Division of Radiology and Nuclear Medicine, Oslo University HospitalThe Centre for Old Age Psychiatry Research, Innlandet Hospital TrustDivision of Radiology and Nuclear Medicine, Oslo University HospitalDivision of Radiology and Nuclear Medicine, Oslo University HospitalDivision of Radiology and Nuclear Medicine, Oslo University HospitalDivision of Radiology and Nuclear Medicine, Oslo University HospitalDivision of Radiology and Nuclear Medicine, Oslo University HospitalOslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University HospitalDivision of Clinical Nutrition, Faculty of Medicine, University of OsloDivision of Radiology and Nuclear Medicine, Oslo University HospitalAbstract 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.http://link.springer.com/article/10.1186/s41747-019-0122-5Body compositionAbdominal fatSkeletal muscleTomography (X-ray computed)Observer variation
collection DOAJ
language English
format Article
sources DOAJ
author Lisa Jannicke Kjønigsen
Magnus Harneshaug
Ann-Monica Fløtten
Lena Korsmo Karterud
Kent Petterson
Grethe Skjolde
Heidi B. Eggesbø
Harald Weedon-Fekjær
Hege Berg Henriksen
Peter M. Lauritzen
spellingShingle Lisa Jannicke Kjønigsen
Magnus Harneshaug
Ann-Monica Fløtten
Lena Korsmo Karterud
Kent Petterson
Grethe Skjolde
Heidi B. Eggesbø
Harald Weedon-Fekjær
Hege Berg Henriksen
Peter M. Lauritzen
Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
European Radiology Experimental
Body composition
Abdominal fat
Skeletal muscle
Tomography (X-ray computed)
Observer variation
author_facet Lisa Jannicke Kjønigsen
Magnus Harneshaug
Ann-Monica Fløtten
Lena Korsmo Karterud
Kent Petterson
Grethe Skjolde
Heidi B. Eggesbø
Harald Weedon-Fekjær
Hege Berg Henriksen
Peter M. Lauritzen
author_sort Lisa Jannicke Kjønigsen
title 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_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_sort reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study
publisher SpringerOpen
series European Radiology Experimental
issn 2509-9280
publishDate 2019-10-01
description Abstract 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.
topic Body composition
Abdominal fat
Skeletal muscle
Tomography (X-ray computed)
Observer variation
url http://link.springer.com/article/10.1186/s41747-019-0122-5
work_keys_str_mv AT lisajannickekjønigsen reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT magnusharneshaug reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT annmonicafløtten reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT lenakorsmokarterud reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT kentpetterson reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT gretheskjolde reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT heidibeggesbø reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT haraldweedonfekjær reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT hegeberghenriksen reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
AT petermlauritzen reproducibilityofsemiautomatedbodycompositionsegmentationofabdominalcomputedtomographyamultiobserverstudy
_version_ 1724674890643013632