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Analyzing magnetic resonance imaging data from glioma patients using deep learning

Authors
  • Menze, Bjoern1
  • Isensee, Fabian2
  • Wiest, Roland3
  • Wiestler, Bene4
  • Maier-Hein, Klaus2
  • Reyes, Mauricio5
  • Bakas, Spyridon6
  • 1 Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
  • 2 DKFZ, Heidelberg, Germany
  • 3 Support Center for Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
  • 4 Neuroradiology, TUM, Munich, Germany
  • 5 Data Science Center, Inselspital, Bern, Switzerland
  • 6 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
Type
Published Article
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Date
Dec 02, 2020
Volume
88
Pages
101828–101828
Identifiers
DOI: 10.1016/j.compmedimag.2020.101828
PMID: 33571780
PMCID: PMC8040671
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.

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