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dc.contributor.authorBouget, David Nicolas Jean-Marie
dc.contributor.authorEijgelaar, Roelant
dc.contributor.authorPedersen, André
dc.contributor.authorKommers, Ivar
dc.contributor.authorArdon, Hilko
dc.contributor.authorBarkhof, Frederik
dc.contributor.authorBello, Lorenzo
dc.contributor.authorBerger, Mitchel S.
dc.contributor.authorNibali, Marco Conti
dc.contributor.authorFurtner, Julia
dc.contributor.authorFyllingen, Even Hovig
dc.contributor.authorHervey-Jumper, Shawn
dc.contributor.authorIdema, Albert J. S.
dc.contributor.authorKiesel, Barbara
dc.contributor.authorKloet, Alfred
dc.contributor.authorMandonnet, Emmanuel
dc.contributor.authorMüller, Domenique M. J.
dc.contributor.authorRobe, Pierre
dc.contributor.authorRossi, Marco
dc.contributor.authorSagberg, Lisa Millgård
dc.contributor.authorSciortino, Tommaso
dc.contributor.authorvan den Brink, Wimar A.
dc.contributor.authorWagemakers, Michiel
dc.contributor.authorWidhalm, Georg
dc.contributor.authorWitte, Marnix G.
dc.contributor.authorZwinderman, Aeilko H.
dc.contributor.authorReinertsen, Ingerid
dc.contributor.authorHamer, Philip C De Witt
dc.contributor.authorSolheim, Ole
dc.identifier.citationCancers, 13, (18), 4674.en_US
dc.description.abstractFor patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleGlioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Tasken_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal