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Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study
Purpose To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume. Methods This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and sub...
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Published in: | Abdominal imaging 2017-02, Vol.42 (2), p.478-489 |
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creator | Gotra, Akshat Chartrand, Gabriel Vu, Kim-Nhien Vandenbroucke-Menu, Franck Massicotte-Tisluck, Karine de Guise, Jacques A. Tang, An |
description | Purpose
To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.
Methods
This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland–Altman analysis. Total interaction time was recorded.
Results
Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: −187 to 247 ml) for MRI and −10 ± 143 ml (−153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was –14 ± 136 ml (−150 to 122 ml) for MRI and 50 ± 226 ml (−176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (−37 to 57 ml) to 2 ± 214 ml (−212 to 216 ml) for MRI and 9 ± 45 ml (−36 to 54 ml) to −46 ± 183 ml (−229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min,
p
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doi_str_mv | 10.1007/s00261-016-0912-7 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1859738825</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4317629881</sourcerecordid><originalsourceid>FETCH-LOGICAL-c415t-11868b034d27eeb85d91bb28e4f0d66284d5b3290db948e120ef57e74f1c00b93</originalsourceid><addsrcrecordid>eNp1kM1KxDAURoMojug8gBspuHFTvUmTJnUngz8DiiAjuAvJ5HaotM2YtIJvb3RURHCVfMm5X8Ih5JDCKQWQZxGAlTQHWuZQUZbLLbLHijIlEGr7Z8-fJmQa4zNAIgWlTOySCZOlSpnvkcXMd2sTmuj7zNfZ3cM8z0zvstkityaiyyJ2jRkH35khpbZ5xZDOVh32gxka359nJns1beM-UxaH0b0dkJ3atBGnX-s-eby6XMxu8tv76_ns4jZfciqGnFJVKgsFd0wiWiVcRa1lCnkNriyZ4k7YglXgbMUVUgZYC4mS13QJYKtin5xsetfBv4wYB901cYlta3r0Y9RUiUoWSjGR0OM_6LMfQ59-lyjJJK-KT4puqGXwMQas9To0nQlvmoL-sK431nVyqT-sa5lmjr6aR9uh-5n4dpwAtgFiuupXGH49_W_rOwTVirg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1872749325</pqid></control><display><type>article</type><title>Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study</title><source>Springer Link</source><creator>Gotra, Akshat ; Chartrand, Gabriel ; Vu, Kim-Nhien ; Vandenbroucke-Menu, Franck ; Massicotte-Tisluck, Karine ; de Guise, Jacques A. ; Tang, An</creator><creatorcontrib>Gotra, Akshat ; Chartrand, Gabriel ; Vu, Kim-Nhien ; Vandenbroucke-Menu, Franck ; Massicotte-Tisluck, Karine ; de Guise, Jacques A. ; Tang, An</creatorcontrib><description>Purpose
To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.
Methods
This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland–Altman analysis. Total interaction time was recorded.
Results
Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: −187 to 247 ml) for MRI and −10 ± 143 ml (−153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was –14 ± 136 ml (−150 to 122 ml) for MRI and 50 ± 226 ml (−176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (−37 to 57 ml) to 2 ± 214 ml (−212 to 216 ml) for MRI and 9 ± 45 ml (−36 to 54 ml) to −46 ± 183 ml (−229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min,
p
< 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min,
p
< 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).
Conclusion
MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.</description><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-016-0912-7</identifier><identifier>PMID: 27680014</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Contrast Media ; Cross-Sectional Studies ; Female ; Gastroenterology ; Hepatology ; Humans ; Image Interpretation, Computer-Assisted ; Imaging ; Imaging, Three-Dimensional ; Liver Diseases - diagnostic imaging ; Magnetic Resonance Imaging - methods ; Male ; Medicine ; Medicine & Public Health ; Meglumine - analogs & derivatives ; Middle Aged ; Organ Size ; Organometallic Compounds ; Pancreatic Diseases - diagnostic imaging ; Radiology ; Reproducibility of Results ; Retrospective Studies ; Tomography, X-Ray Computed - methods</subject><ispartof>Abdominal imaging, 2017-02, Vol.42 (2), p.478-489</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>Abdominal Radiology is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-11868b034d27eeb85d91bb28e4f0d66284d5b3290db948e120ef57e74f1c00b93</citedby><cites>FETCH-LOGICAL-c415t-11868b034d27eeb85d91bb28e4f0d66284d5b3290db948e120ef57e74f1c00b93</cites><orcidid>0000-0001-8967-5503</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27680014$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gotra, Akshat</creatorcontrib><creatorcontrib>Chartrand, Gabriel</creatorcontrib><creatorcontrib>Vu, Kim-Nhien</creatorcontrib><creatorcontrib>Vandenbroucke-Menu, Franck</creatorcontrib><creatorcontrib>Massicotte-Tisluck, Karine</creatorcontrib><creatorcontrib>de Guise, Jacques A.</creatorcontrib><creatorcontrib>Tang, An</creatorcontrib><title>Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.
Methods
This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland–Altman analysis. Total interaction time was recorded.
Results
Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: −187 to 247 ml) for MRI and −10 ± 143 ml (−153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was –14 ± 136 ml (−150 to 122 ml) for MRI and 50 ± 226 ml (−176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (−37 to 57 ml) to 2 ± 214 ml (−212 to 216 ml) for MRI and 9 ± 45 ml (−36 to 54 ml) to −46 ± 183 ml (−229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min,
p
< 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min,
p
< 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).
Conclusion
MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.</description><subject>Contrast Media</subject><subject>Cross-Sectional Studies</subject><subject>Female</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Imaging</subject><subject>Imaging, Three-Dimensional</subject><subject>Liver Diseases - diagnostic imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Meglumine - analogs & derivatives</subject><subject>Middle Aged</subject><subject>Organ Size</subject><subject>Organometallic Compounds</subject><subject>Pancreatic Diseases - diagnostic imaging</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1kM1KxDAURoMojug8gBspuHFTvUmTJnUngz8DiiAjuAvJ5HaotM2YtIJvb3RURHCVfMm5X8Ih5JDCKQWQZxGAlTQHWuZQUZbLLbLHijIlEGr7Z8-fJmQa4zNAIgWlTOySCZOlSpnvkcXMd2sTmuj7zNfZ3cM8z0zvstkityaiyyJ2jRkH35khpbZ5xZDOVh32gxka359nJns1beM-UxaH0b0dkJ3atBGnX-s-eby6XMxu8tv76_ns4jZfciqGnFJVKgsFd0wiWiVcRa1lCnkNriyZ4k7YglXgbMUVUgZYC4mS13QJYKtin5xsetfBv4wYB901cYlta3r0Y9RUiUoWSjGR0OM_6LMfQ59-lyjJJK-KT4puqGXwMQas9To0nQlvmoL-sK431nVyqT-sa5lmjr6aR9uh-5n4dpwAtgFiuupXGH49_W_rOwTVirg</recordid><startdate>20170201</startdate><enddate>20170201</enddate><creator>Gotra, Akshat</creator><creator>Chartrand, Gabriel</creator><creator>Vu, Kim-Nhien</creator><creator>Vandenbroucke-Menu, Franck</creator><creator>Massicotte-Tisluck, Karine</creator><creator>de Guise, Jacques A.</creator><creator>Tang, An</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8967-5503</orcidid></search><sort><creationdate>20170201</creationdate><title>Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study</title><author>Gotra, Akshat ; Chartrand, Gabriel ; Vu, Kim-Nhien ; Vandenbroucke-Menu, Franck ; Massicotte-Tisluck, Karine ; de Guise, Jacques A. ; Tang, An</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-11868b034d27eeb85d91bb28e4f0d66284d5b3290db948e120ef57e74f1c00b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Contrast Media</topic><topic>Cross-Sectional Studies</topic><topic>Female</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted</topic><topic>Imaging</topic><topic>Imaging, Three-Dimensional</topic><topic>Liver Diseases - diagnostic imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Meglumine - analogs & derivatives</topic><topic>Middle Aged</topic><topic>Organ Size</topic><topic>Organometallic Compounds</topic><topic>Pancreatic Diseases - diagnostic imaging</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gotra, Akshat</creatorcontrib><creatorcontrib>Chartrand, Gabriel</creatorcontrib><creatorcontrib>Vu, Kim-Nhien</creatorcontrib><creatorcontrib>Vandenbroucke-Menu, Franck</creatorcontrib><creatorcontrib>Massicotte-Tisluck, Karine</creatorcontrib><creatorcontrib>de Guise, Jacques A.</creatorcontrib><creatorcontrib>Tang, An</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database (ProQuest)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Biological Science Journals</collection><collection>Biochemistry Abstracts 1</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gotra, Akshat</au><au>Chartrand, Gabriel</au><au>Vu, Kim-Nhien</au><au>Vandenbroucke-Menu, Franck</au><au>Massicotte-Tisluck, Karine</au><au>de Guise, Jacques A.</au><au>Tang, An</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2017-02-01</date><risdate>2017</risdate><volume>42</volume><issue>2</issue><spage>478</spage><epage>489</epage><pages>478-489</pages><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.
Methods
This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland–Altman analysis. Total interaction time was recorded.
Results
Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: −187 to 247 ml) for MRI and −10 ± 143 ml (−153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was –14 ± 136 ml (−150 to 122 ml) for MRI and 50 ± 226 ml (−176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (−37 to 57 ml) to 2 ± 214 ml (−212 to 216 ml) for MRI and 9 ± 45 ml (−36 to 54 ml) to −46 ± 183 ml (−229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min,
p
< 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min,
p
< 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).
Conclusion
MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>27680014</pmid><doi>10.1007/s00261-016-0912-7</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-8967-5503</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Contrast Media Cross-Sectional Studies Female Gastroenterology Hepatology Humans Image Interpretation, Computer-Assisted Imaging Imaging, Three-Dimensional Liver Diseases - diagnostic imaging Magnetic Resonance Imaging - methods Male Medicine Medicine & Public Health Meglumine - analogs & derivatives Middle Aged Organ Size Organometallic Compounds Pancreatic Diseases - diagnostic imaging Radiology Reproducibility of Results Retrospective Studies Tomography, X-Ray Computed - methods |
title | Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study |
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