State of the art: Machine learning applications in Glioma Imaging

Eyal Lotan; Rajan Jain; Narges Razavian; Girish M. Fatterpekar; Yvonne W. Lui

doi:10.2214/AJR.18.20218

State of the art: Machine learning applications in Glioma Imaging

Eyal Lotan, Rajan Jain, Narges Razavian, Girish M. Fatterpekar, Yvonne W. Lui^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

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.

Original language	English
Pages (from-to)	26-37
Number of pages	12
Journal	American Journal of Roentgenology
Volume	212
Issue number	1
DOIs	https://doi.org/10.2214/AJR.18.20218
State	Published - Jan 2019
Externally published	Yes

Keywords

Brain lesion segmentation
Deep learning
Glioma
Machine learning
Radiomics

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title = "State of the art: Machine learning applications in Glioma Imaging",

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.",

keywords = "Brain lesion segmentation, Deep learning, Glioma, Machine learning, Radiomics",

author = "Eyal Lotan and Rajan Jain and Narges Razavian and Fatterpekar, {Girish M.} and Lui, {Yvonne W.}",

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year = "2019",

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AU - Lotan, Eyal

AU - Jain, Rajan

AU - Razavian, Narges

AU - Fatterpekar, Girish M.

AU - Lui, Yvonne W.

N1 - Publisher Copyright: © American Roentgen Ray Society.

PY - 2019/1

Y1 - 2019/1

N2 - 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.

AB - 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.

KW - Brain lesion segmentation

KW - Deep learning

KW - Glioma

KW - Machine learning

KW - Radiomics

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JF - American Journal of Roentgenology

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State of the art: Machine learning applications in Glioma Imaging

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Keywords

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