Analyzing magnetic resonance imaging data from glioma patients using deep learning

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.

Automated summary

The quantitative analysis of brain tumor images using computational tools, particularly machine learning and deep learning algorithms, has become increasingly popular. This review focuses on diagnostic biomarkers for glioma and publicly available resources, with an emphasis on the Multimodal Brain Tumor Segmentation (BraTS) Challenge. It also discusses state-of-the-art methods in glioma image segmentation, highlighting publicly available tools and deep learning algorithms.