Research graph
References from Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI. Local targets link to admitted publications; unresolved targets remain external evidence.
Deep learning for brain MRI segmentation: state of the art and future directions
10.1007/s10278-017-9983-4 · 2017 · External reference
Application of convolutional neural network in segmenting brain regions from MRI data
2019 · External reference
Brain atrophy associations with white matter lesions in the ageing brain: the Lothian Birth Cohort 1936
10.1007/s00330-012-2677-x · 2013 · External reference
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
10.1016/j.media.2007.06.004 · 2008 · External reference
A reproducible evaluation of ANTs similarity metric performance in brain image registration
10.1016/j.neuroimage.2010.09.025 · 2011 · External reference
Fully convolutional network for liver segmentation and lesions detection
2016 · External reference
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
10.1016/j.artmed.2018.08.008 · 2019 · External reference
Fluid-attenuated inversion recovery (FLAIR) for assessment of cerebral infarction: initial clinical experience in 50 patients
10.1161/01.str.27.7.1187 · 1996 · External reference
Unresolved reference
2010 · External reference
Unresolved reference
2016 · External reference
VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images
10.1016/j.neuroimage.2017.04.041 · 2018 · External reference
The comprehensive assessment of neurodegeneration and dementia: Canadian cohort study
10.1017/cjn.2019.27 · 2019 · External reference
Unresolved reference
2018 · External reference
Automated brain extraction from T2-weighted magnetic resonance images
10.1002/jmri.22510 · 2011 · External reference
Automatic segmentation OF brain tissue and white matter lesions IN MRI
2007 · External reference
Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study
10.1016/j.neuroimage.2017.09.011 · 2017 · External reference
CompNet: complementary segmentation network for brain MRI extraction
2018 · External reference
Brain extraction methods for neurological FLAIR MRI
2018 · External reference
Unresolved reference
2018 · External reference
The importance of skip connections in biomedical image segmentation
2016 · External reference
The Canadian dementia imaging protocol: harmonizing national cohorts
10.1002/jmri.26197 · 2019 · External reference
The Canadian dementia imaging protocol: harmonizing national cohorts
10.1002/jmri.26197 · 2019 · External reference
BEaST: brain extraction based on nonlocal segmentation technique
10.1016/j.neuroimage.2011.09.012 · 2012 · External reference
Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging
10.1016/j.media.2012.09.004 · 2013 · External reference
White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
10.1016/j.nicl.2017.12.022 · 2018 · External reference
Methodological considerations for measuring rates of brain atrophy
10.1002/jmri.10325 · 2003 · External reference
White matter hyperintensities extraction based T2-FLAIR MRI using non-local means filter and nearest neighbor algorithm
2014 · External reference
How does the accuracy of intracranial volume measurements affect normalized brain volumes? Sample size estimates based on 966 subjects from the HUNT MRI cohort
10.3174/ajnr.a4299 · 2015 · External reference
Deep residual learning for image recognition
2016 · External reference
October). Identity mappings in deep residual networks
2016 · External reference
Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset
10.1038/s41598-019-52966-0 · 2019 · External reference
Densely connected convolutional networks
2017 · External reference
3D U-Net for skull stripping in brain MRI
10.3390/app9030569 · 2019 · External reference
MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation
10.1016/j.neunet.2019.08.025 · 2020 · External reference
Robust brain extraction across datasets and comparison with publicly available methods
10.1109/tmi.2011.2138152 · 2011 · External reference
Unresolved reference
2015 · External reference
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
10.1002/jmri.21049 · 2008 · External reference
The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation
2017 · External reference
Methods on skull stripping of MRI head scan images—a review
10.1007/s10278-015-9847-8 · 2016 · External reference
Robust white matter lesion segmentation in FLAIR MRI
10.1109/tbme.2011.2181167 · 2011 · External reference
Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets
10.1016/j.mri.2019.08.022 · 2020 · External reference
Deep MRI brain extraction: a 3D convolutional neural network for skull stripping
10.1016/j.neuroimage.2016.01.024 · 2016 · External reference
Optimized high resolution 3d dense-u-net network for brain and spine segmentation
10.3390/app9030404 · 2019 · External reference
Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration
10.1212/wnl.0b013e318281ccd3 · 2013 · External reference
H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes
10.1109/tmi.2018.2845918 · 2018 · External reference
Fully convolutional networks for semantic segmentation
2015 · External reference
Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance
10.1016/j.neuroimage.2014.09.034 · 2015 · External reference
Nonlocal intracranial cavity extraction
10.1155/2014/820205 · 2014 · External reference
Epidemiology of alzheimer disease
10.1101/cshperspect.a006239 · 2012 · External reference
National neuroinformatics framework for canadian consortium on neurodegeneration in aging (CCNA)
10.3389/fninf.2018.00085 · 2018 · External reference
Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning
10.1016/j.mri.2019.10.003 · 2020 · External reference
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
10.1111/j.1532-5415.2005.53221.x · 2005 · External reference
Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements
10.1016/j.neuroimage.2013.06.068 · 2013 · External reference
Unresolved reference
2020 · External reference
Classifying dementia using local binary patterns from different regions in magnetic resonance images
10.1155/2015/572567 · 2015 · External reference
Conventional and deep learning methods for skull stripping in brain MRI
10.3390/app10051773 · 2020 · External reference
Conventional and deep learning methods for skull stripping in brain MRI
10.3390/app10051773 · 2020 · External reference
Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets
10.1016/j.mri.2019.05.001 · 2019 · External reference
Brain MRI atrophy quantification in MS: from methods to clinical application
10.1212/wnl.0000000000003542 · 2017 · External reference
October). U-net: convolutional networks for biomedical image segmentation
2015 · External reference
A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging
10.1001/archneur.60.7.989 · 2003 · External reference
A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity
10.1016/j.nicl.2016.05.017 · 2016 · External reference
A hybrid approach to the skull stripping problem in MRI
10.1016/j.neuroimage.2004.03.032 · 2004 · External reference
Brain tissue volumes in the general population of the elderly: the AGES-Reykjavik study
10.1016/j.neuroimage.2011.11.024 · 2012 · External reference
Changes in premorbid brain volume predict Alzheimer’s disease pathology
10.1212/01.wnl.0000079053.77227.14 · 2003 · External reference
Fast robust automated brain extraction
10.1002/hbm.10062 · 2002 · External reference
Therapeutic strategies and drug development for vascular cognitive impairment
10.1161/jaha.117.005568 · 2017 · External reference
Feature space analysis: effects of MRI protocols
10.1118/1.1414306 · 2001 · External reference
Epidemiology of dementias and Alzheimer’s disease
10.1016/j.arcmed.2012.11.003 · 2012 · External reference
Distinct brain volume changes correlating with clinical stage, disease progression rate, mutation size, and age at onset prediction as early biomarkers of brain atrophy in Huntington’s disease
10.1111/j.1755-5949.2008.00068.x · 2009 · External reference
Unresolved reference
2020 · External reference
Rethinking the inception architecture for computer vision
2016 · External reference
Atherosclerosis imaging and the Canadian atherosclerosis imaging network
2013 · External reference
Brain extraction on MRI scans in presence of diffuse glioma: multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training
10.1016/j.neuroimage.2020.117081 · 2020 · External reference
What are white matter hyperintensities made of? Relevance to vascular cognitive impairment
10.1161/jaha.114.001140 · 2015 · External reference
Unresolved reference
External reference
Skip connection U-net for white matter hyperintensities segmentation from MRI
10.1109/access.2019.2948476 · 2019 · External reference
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
10.1016/j.neuroimage.2006.01.015 · 2006 · External reference
June). Automatic skull stripping in brain MRI based on local moment of inertia structure tensor
2012 · External reference
