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The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyagerTM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T1-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. 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"Glioblastoma Segmentation: Comparison of Three Different Software Packages." ROHub. 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L., Berntsen, E. M., Solheim, O., Reinertsen, I. (2016).Glioblastoma Segmentation: Comparison of Three Different Software Packages [Data set]. Norstore. https://doi.org/10.11582/2016.00004 https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://purl.org/dc/terms/rightsHolder USIGT (USIGT) https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://purl.org/dc/terms/type Image https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/author mailto:georgehadib@gmail.com https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/contentUrl https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2016.00004 https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/creator mailto:georgehadib@gmail.com https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/dateCreated 2016-10-11 00:00:00 https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/dateModified 2022-03-22 00:58:14.814982+00:00 https://w3id.org/ro-id/a8c21c10-47aa-4145-a2f2-173f1c501773/resources/b1dd0cba-10c4-4433-a1aa-b06ace1dff57 http://schema.org/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 BrainVoyagerTM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T1-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. 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