Related Experiment Video
Updated: Feb 3, 2026

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
State of the Art: Machine Learning Applications in Glioma Imaging
Eyal Lotan1, Rajan Jain1, Narges Razavian1,2
11 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
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.
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