Journal
AMERICAN JOURNAL OF ROENTGENOLOGY
Volume 212, Issue 1, Pages 26-37Publisher
AMER ROENTGEN RAY SOC
DOI: 10.2214/AJR.18.20218
Keywords
brain lesion segmentation; deep learning; glioma; machine learning; radiomics
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available