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

Author:

Ebner Michael,Wang Guotai,Li Wenqi,Aertsen Michael,Patel Premal A.,Aughwane Rosalind,Melbourne Andrew,Doel Tom,Dymarkowski Steven,De Coppi Paolo,David Anna L.,Deprest Jan,Ourselin Sébastien,Vercauteren Tom

Funder

Wellcome Trust

Engineering and Physical Sciences Research Council

National Institute for Health Research University College London Hospitals Biomedical Research Centre

Great Ormond Street Hospital Charity fund

stipend of the Klinische Onderzoeks en Opleidings-Raad UZ Leuven

Medtronic / Royal Academy of Engineering Research Chair

Publisher

Elsevier BV

Subject

Cognitive Neuroscience,Neurology

Reference60 articles.

1. Reliability of MR imaging-based posterior fossa and brain stem measurements in open spinal dysraphism in the era of fetal surgery;Aertsen;Am. J. Neuroradiol.,2019

2. PVR: patch-to-volume reconstruction for large area motion correction of fetal MRI;Alansary;IEEE Trans. Med. Imaging,2017

3. Automatic segmentation of head structures on fetal MRI;Anquez,2009

4. Robust principal component analysis?;Candès;J. Assoc. Comput. Mach.,2011

5. 3D U-net: learning dense volumetric segmentation from sparse annotation;Çiçek,2016

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