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Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
“Joint segmentation of multiple sclerosis lesions and brain anatomy in MRI scans of any contrast and resolution with CNNs,”
B Billot, S Cerri, et al., · 1974
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“Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain,”
B Fischl, D Salat, et al., · 2002
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“Gray matter atrophy in multiple sclerosis: a longitudinal study,”
E Fisher, J Lee, et al., · 2008
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“The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods,”
CR Jack Jr, MA Bernstein, et al., · 2008
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“A bayesian model of shape and appearance for subcortical brain segmentation,”
B Patenaude, SM Smith, et al., · 2011
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“The LST toolbox for lesion segmentation and quantification,”
P Schmidt, C Gaser, et al., · 2012
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“The WU-Minn human connectome project: an overview,”
DC Van Essen, SM Smith, et al., · 2013
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“U-net: Convolutional networks for biomedical image segmentation,”
O Ronneberger, P Fischer, et al., · 2015
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“Bianca (brain intensity abnormality classification algorithm): A new tool for automated segmentation of white matter hyperintensities,”
L Griffanti, G Zamboni, et al., · 2016
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“Fast and sequence-adaptive whole-brain segmentation using parametric bayesian modeling,”
O Puonti, JE Iglesias, et al., · 2016
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“Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to MS lesion segmentation,”
T Brosch, LYW Tang, et al., · 2016
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“Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities,”
M Ghafoorian, N Karssemeijer, et al., · 2017
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“Longitudinal MS lesion segmentation data,”
A Carass, S Roy, et al., · 2017
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“The neuro bureau ADHD-200 repository,”
P Bellec, C Chu, et al., · 2017
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Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging
“Multiple sclerosis–a review,”
R Dobson and G Giovannoni, · 2019
Later among the works it cites.
“QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy,”
A Roy, S Conjeti, et al., · 2019
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“Standardized assessment of automatic segmentation of white matter hyperintensities; results of the wmh segmentation challenge,”
HJ Kuijf, JM Biesbroek, et al., · 2019
Later among the works it cites.
“Fastsurfer – a fast and accurate deep learning based neuroimaging pipeline,”
L Henschel, S Conjeti, et al., · 2020
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“A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in MS,”
S Cerri, O Puonti, et al., · 2021
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“Identification of white matter hyperintensities in routine emergency department visits using portable bedside magnetic resonance imaging,”
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P Schmidt, · 2017
Cited alongside, same era.
“Deep visual domain adaptation: A survey,”
M Wang and W Deng, · 2018
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“Group normalization,”
Y Wu and K He, · 2018
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“Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure,”
O Commowick, A Istace, et al., · 2018
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A de Havenon, NR Parasuram, , et al., · 2023
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“SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining,”
B Billot, DN Greve, et al., · 2023
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“SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry,”
Iglesias JE, B Billot, et al., · 2023
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