Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change
暂无分享,去创建一个
M. Jorge Cardoso | Owen T. Carmichael | Ian B. Malone | Josephine Barnes | Geert Jan Biessels | Cassidy M. Fiford | Carole H. Sudre | Hugh Pemberton | Phoebe Walsh | Emily Manning | Jennifer Nicholas | Willem H Bouvy | Jennifer M. Nicholas | Emily N. Manning | Owen Carmichael | J. Barnes | C. Sudre | G. Biessels | I. Malone | J. Nicholas | W. Bouvy | H. Pemberton | Phoebe Walsh | M. Cardoso
[1] Lars Kai Hansen,et al. Segmentation of age-related white matter changes in a clinical multi-center study , 2008, NeuroImage.
[2] Clifford R. Jack,et al. EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Segmentation and for the Alzheimer ’ s Disease Neuroimaging Initiative , 2015 .
[3] I. Moseley,et al. Fast FLAIR of the brain: the range of appearances in normal subjects and its application to quantification of white-matter disease , 1997, Neuroradiology.
[4] Alexander Leemans,et al. Disruption of the Cerebral White Matter Network Is Related to Slowing of Information Processing Speed in Patients With Type 2 Diabetes , 2013, Diabetes.
[5] Joanna M Wardlaw,et al. What are White Matter Hyperintensities Made of? , 2015, Journal of the American Heart Association.
[6] Sébastien Ourselin,et al. Brain MAPS: An automated, accurate and robust brain extraction technique using a template library , 2011, NeuroImage.
[7] J. Kaye,et al. Impact of white matter hyperintensity volume progression on rate of cognitive and motor decline , 2008, Neurology.
[8] Thomas Samaille,et al. Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation , 2012, PloS one.
[9] A. Folsom,et al. Cerebral MRI findings and cognitive functioning , 2005, Neurology.
[10] Ji Won Han,et al. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images , 2014, Neuroradiology.
[11] Sébastien Ourselin,et al. Global image registration using a symmetric block-matching approach , 2014, Journal of medical imaging.
[12] Tien Yin Wong,et al. Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts , 2012, NeuroImage.
[13] Nick C. Fox,et al. Vascular and Alzheimer's disease markers independently predict brain atrophy rate in Alzheimer's Disease Neuroimaging Initiative controls , 2013, Neurobiology of Aging.
[14] Liana G. Apostolova,et al. Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance , 2015, Alzheimer's & Dementia.
[15] Anil F. Ramlackhansingh,et al. Lesion identification using unified segmentation-normalisation models and fuzzy clustering , 2008, NeuroImage.
[16] Sébastien Ourselin,et al. Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation , 2015, IEEE Transactions on Medical Imaging.
[17] Daniel Rueckert,et al. Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion , 2015, IEEE Transactions on Medical Imaging.
[18] P. Scheltens,et al. White matter hyperintensities, cognitive impairment and dementia: an update , 2015, Nature Reviews Neurology.
[19] Ludovica Griffanti,et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.
[20] D. Selkoe. Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.
[21] Johan H. C. Reiber,et al. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly , 2005, NeuroImage.
[22] Paolo Perrotta,et al. Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review , 2015, Neuroinformatics.
[23] A. Hofman,et al. Periventricular cerebral white matter lesions predict rate of cognitive decline , 2002, Annals of neurology.
[24] Robert Perneczky,et al. Head circumference, atrophy and cognition: implications for brain reserve in Alzheimer's disease , 2011, Alzheimer's & Dementia.
[25] Sébastien Ourselin,et al. Longitudinal segmentation of age‐related white matter hyperintensities , 2017, Medical Image Anal..
[26] Henri A. Vrooman,et al. Progression of Cerebral Small Vessel Disease in Relation to Risk Factors and Cognitive Consequences: Rotterdam Scan Study , 2008, Stroke.
[27] Owen Carmichael,et al. Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. , 2010, Archives of neurology.
[28] Simon C. Potter,et al. A Genome-Wide Association Search for Type 2 Diabetes Genes in African Americans , 2012, PLoS ONE.
[29] Nick C Fox,et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.
[30] Stephen C Strother,et al. The effect of white matter hyperintensities on verbal memory , 2018, Neurology.
[31] C. Jack,et al. Alzheimer's Disease Neuroimaging Initiative , 2008 .
[32] W. M. van der Flier,et al. Medial temporal lobe atrophy and white matter hyperintensities are associated with mild cognitive deficits in non-disabled elderly people: the LADIS study , 2005, Journal of Neurology, Neurosurgery & Psychiatry.
[33] Sébastien Ourselin,et al. The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging , 2014, International Journal of Computer Assisted Radiology and Surgery.
[34] M. Jorge Cardoso,et al. White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy , 2017, Hippocampus.
[35] Frithjof Kruggel,et al. White matter lesion segmentation based on feature joint occurrence probability and chi2 random field theory from magnetic resonance (MR) images , 2010, Pattern Recognit. Lett..
[36] Alijavad Moosavi,et al. Air column in esophagus and symptoms of gastroesophageal reflux disease , 2012, BMC Medical Imaging.
[37] Koen L. Vincken,et al. Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.
[38] Jian Chen,et al. Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities , 2009, NeuroImage.
[39] J. Whitwell,et al. Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.
[40] Stefan Wagenpfeil,et al. Brain size and the compensation of Alzheimer's disease symptoms: A longitudinal cohort study , 2013, Alzheimer's & Dementia.
[41] Robert Zivadinov,et al. Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates , 2012, BMC Medical Imaging.
[42] Wiro J. Niessen,et al. White matter lesion extension to automatic brain tissue segmentation on MRI , 2009, NeuroImage.
[43] Frederik Barkhof,et al. White Matter Hyperintensities Relate to Clinical Progression in Subjective Cognitive Decline , 2015, Stroke.
[44] Koenraad Van Leemput,et al. Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.
[45] F. Barkhof,et al. Bullseye's representation of cerebral white matter hyperintensities , 2017, Journal of neuroradiology. Journal de neuroradiologie.
[46] D. Louis Collins,et al. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging , 2017, NeuroImage.
[47] R. Bakshi,et al. Intraventricular CSF pulsation artifact on fast fluid-attenuated inversion-recovery MR images: analysis of 100 consecutive normal studies. , 2000, AJNR. American journal of neuroradiology.