An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the bra...

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Bibliographic Details
Main Authors: Michael Ebner, Guotai Wang, Wenqi Li, Michael Aertsen, Premal A. Patel, Rosalind Aughwane, Andrew Melbourne, Tom Doel, Steven Dymarkowski, Paolo De Coppi, Anna L. David, Jan Deprest, Sébastien Ourselin, Tom Vercauteren
Format: Article
Language:English
Published: Elsevier 2020-02-01
Series:NeuroImage
Subjects:
Fetal MRI
Deep learning
Super resolution
Convolutional neural network
Brain localization
Segmentation
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811919309152

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http://www.sciencedirect.com/science/article/pii/S1053811919309152

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