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Analyzing magnetic resonance imaging data from glioma patients using deep learning.


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.

SUBMITTER: Menze B 

PROVIDER: S-EPMC8040671 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Analyzing magnetic resonance imaging data from glioma patients using deep learning.

Menze Bjoern B   Isensee Fabian F   Wiest Roland R   Wiestler Bene B   Maier-Hein Klaus K   Reyes Mauricio M   Bakas Spyridon S  

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 20201202


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 availa  ...[more]

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