Eyal Lotan1, Rajan Jain1, Narges Razavian1,2, Girish M Fatterpekar1, Yvonne W Lui1. 1. 1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016. 2. 2 Population Health Department, NYU Langone Medical Center, New York, NY.
Abstract
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION: We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION: We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.
Entities:
Keywords:
brain lesion segmentation; deep learning; glioma; machine learning; radiomics
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