Analyzing magnetic resonance imaging data from glioma patients using deep learning

• Published in: Computerized medical imaging and graphics Vol. 88; p. 101828
• Main Authors: Menze, Bjoern, Isensee, Fabian, Wiest, Roland, Wiestler, Bene, Maier-Hein, Klaus, Reyes, Mauricio, Bakas, Spyridon
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2021; Elsevier Science Ltd
• Subjects: Algorithms; Biomarkers; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain tumor; Brain tumor segmentation challenge; Brain tumors; BraTS; Computational neuroscience; Deep Learning; Diagnosis; Glioma; Glioma - diagnostic imaging; Humans; Image acquisition; Image processing; Image Processing, Computer-Assisted; Image quantification; Image segmentation; Learning algorithms; Machine learning; Magnetic Resonance Imaging; Medical imaging; Neural Networks, Computer; Neuroimaging; NeuroOncology; Patients; Software; State-of-the-art reviews; Tumors; Brain tumor; Deep learning; Image segmentation; Image quantification; Brain tumor segmentation challenge; Glioma; Machine learning; BraTS; NeuroOncology
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2020.101828

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.