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, p. 101828. Elsevier 10.1016/j.compmedimag.2020.101828

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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.

Item Type:

Journal Article (Review Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

UniBE Contributor:

Wiest, Roland and Reyes, Mauricio

Subjects:

000 Computer science, knowledge & systems
500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0895-6111

Publisher:

Elsevier

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

21 Jan 2021 14:55

Last Modified:

21 Feb 2021 03:00

Publisher DOI:

10.1016/j.compmedimag.2020.101828

PubMed ID:

33571780

BORIS DOI:

10.48350/150796

URI:

https://boris.unibe.ch/id/eprint/150796

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