A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly a...

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Bibliographic Details
Main Authors: Maria Ines Meyer, Ezequiel de la Rosa, Nuno Pedrosa de Barros, Roberto Paolella, Koen Van Leemput, Diana M. Sima
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-08-01
Series:Frontiers in Neuroscience
Subjects:
multi-scanner
magnetic resonance imaging
segmentation
data augmentation
gaussian mixture models
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2021.708196/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2021.708196/full

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