Glioblastoma surgery imaging–reporting and data system: Validation and performance of the automated segmentation task
Bouget, David; Eijgelaar, Roelant S.; Pedersen, André; Kommers, Ivar; Ardon, Hilko; Barkhof, Frederik; Bello, Lorenzo; Berger, Mitchel S.; Nibali, Marco Conti; Furtner, Julia; Fyllingen, Even Hovig; Hervey-Jumper, Shawn; Idema, Albert J.S.; Kiesel, Barbara; Kloet, Alfred; Mandonnet, Emmanuel; Müller, Domenique M.J.; Robe, Pierre A.; Rossi, Marco; Sagberg, Lisa M.; Sciortino, Tommaso; Van den Brink, Wimar A.; Wagemakers, Michiel; Widhalm, Georg; Witte, Marnix G.; Zwinderman, Aeilko H.; Reinertsen, Ingerid; Hamer, Philip C.De Witt; Solheim, Ole
(2021) Cancers, volume 13, issue 18
(Article)
Abstract
For 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.
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In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSIRADS, 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.
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Keywords: 3D segmentation, Computer-assisted image processing, Deep learning, Glioblastoma, Magnetic resonance imaging, Neuroimaging, computer-assisted image processing, deep learning, magnetic resonance imaging, glioblastoma, neuroimaging, Oncology, Cancer Research, Journal Article
ISSN: 2072-6694
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Note: Funding Information: Funding: This research was supported by an unrestricted grant of Stichting Hanarth fonds, “Machine learning for better neurosurgical decisions in patients with glioblastoma”; a grant for public–private partnerships (Amsterdam UMC PPP-grant) sponsored by the Dutch government (Ministry of Economic Affairs) through the Rijksdienst voor Ondernemend Nederland (RVO) and Topsector Life Sciences & Health (LSH), “Picturing predictions for patients with brain tumors”; a grant from the Inno-vative Medical Devices Initiative program, Project Number 10-10400-96-14003; financed by the Netherlands Organisation for Scientific Research (NWO), 2020.027; a grant from the Dutch Cancer Society, VU2014-7113; the Anita Veldman foundation, CCA2018-2-17; the Norwegian National Advisory Unit for Ultrasound and Image-guided Therapy. F.B. is supported by the NIHR biomedical research center at UCLH. Funding Information: Acknowledgments: This work was conducted both at (i) the digital labs at HUNT Cloud, the Norwegian University of Science and Technology, Trondheim, Norway, and (ii) the Dutch national e-infrastructure with the support of SURF Cooperative and the Translational Research IT (TraIT) project, an initiative from the Center for Translational Molecular Medicine (CTMM). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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