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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-10-01
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| Series: | European Radiology Experimental |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s41747-019-0122-5 |
| _version_ | 1818384297950707712 |
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| 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 |
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T03:20:02Z |
| format | Article |
| id | doaj.art-9350d7833f904bef9ed03c723d869ef2 |
| institution | Directory Open Access Journal |
| issn | 2509-9280 |
| language | English |
| last_indexed | 2024-12-14T03:20:02Z |
| publishDate | 2019-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj.art-9350d7833f904bef9ed03c723d869ef22022-12-21T23:19:03ZengSpringerOpenEuropean 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 |
| 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 |
| 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 | Body composition Abdominal fat Skeletal muscle Tomography (X-ray computed) Observer variation |
| url | http://link.springer.com/article/10.1186/s41747-019-0122-5 |
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