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Glioblastoma Segmentation: Comparison of Three Different Software Packages

To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of gliobla...

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Autores principales: Fyllingen, Even Hovig, Stensjøen, Anne Line, Berntsen, Erik Magnus, Solheim, Ole, Reinertsen, Ingerid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5079567/
https://www.ncbi.nlm.nih.gov/pubmed/27780224
http://dx.doi.org/10.1371/journal.pone.0164891
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author Fyllingen, Even Hovig
Stensjøen, Anne Line
Berntsen, Erik Magnus
Solheim, Ole
Reinertsen, Ingerid
author_facet Fyllingen, Even Hovig
Stensjøen, Anne Line
Berntsen, Erik Magnus
Solheim, Ole
Reinertsen, Ingerid
author_sort Fyllingen, Even Hovig
collection PubMed
description To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyager(TM) QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T(1)-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice’s similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from -2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ≥ 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice’s similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting.
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spelling pubmed-50795672016-11-04 Glioblastoma Segmentation: Comparison of Three Different Software Packages Fyllingen, Even Hovig Stensjøen, Anne Line Berntsen, Erik Magnus Solheim, Ole Reinertsen, Ingerid PLoS One Research Article To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyager(TM) QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T(1)-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice’s similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from -2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ≥ 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice’s similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting. Public Library of Science 2016-10-25 /pmc/articles/PMC5079567/ /pubmed/27780224 http://dx.doi.org/10.1371/journal.pone.0164891 Text en © 2016 Fyllingen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fyllingen, Even Hovig
Stensjøen, Anne Line
Berntsen, Erik Magnus
Solheim, Ole
Reinertsen, Ingerid
Glioblastoma Segmentation: Comparison of Three Different Software Packages
title Glioblastoma Segmentation: Comparison of Three Different Software Packages
title_full Glioblastoma Segmentation: Comparison of Three Different Software Packages
title_fullStr Glioblastoma Segmentation: Comparison of Three Different Software Packages
title_full_unstemmed Glioblastoma Segmentation: Comparison of Three Different Software Packages
title_short Glioblastoma Segmentation: Comparison of Three Different Software Packages
title_sort glioblastoma segmentation: comparison of three different software packages
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5079567/
https://www.ncbi.nlm.nih.gov/pubmed/27780224
http://dx.doi.org/10.1371/journal.pone.0164891
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