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Analyzing magnetic resonance imaging data from glioma patients using deep learning


Menze, Bjoern; Isensee, Fabian; Wiest, Roland; Wiestler, Bene; Maier-Hein, Klaus; Reyes, Mauricio; Bakas, Spyridon (2021). Analyzing magnetic resonance imaging data from glioma patients using deep learning. Computerized Medical Imaging and Graphics, 88:101828.

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.

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Radiological and Ultrasound Technology, Health Informatics, Radiology Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
Language:English
Date:1 March 2021
Deposited On:19 Feb 2021 15:52
Last Modified:19 Feb 2021 15:52
Publisher:Elsevier
ISSN:0895-6111
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.compmedimag.2020.101828

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