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Items: 1 to 20 of 102


White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.

Schirmer MD, Dalca AV, Sridharan R, Giese AK, Donahue KL, Nardin MJ, Mocking SJT, McIntosh EC, Frid P, Wasselius J, Cole JW, Holmegaard L, Jern C, Jimenez-Conde J, Lemmens R, Lindgren AG, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Vagal A, Xu H, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Worrall BB, Wu O, Golland P, Rost NS; MRI-GENIE Investigators.

Neuroimage Clin. 2019;23:101884. doi: 10.1016/j.nicl.2019.101884. Epub 2019 May 29.


Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

Moeskops P, de Bresser J, Kuijf HJ, Mendrik AM, Biessels GJ, Pluim JPW, Išgum I.

Neuroimage Clin. 2017 Oct 12;17:251-262. doi: 10.1016/j.nicl.2017.10.007. eCollection 2018.


White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

Guerrero R, Qin C, Oktay O, Bowles C, Chen L, Joules R, Wolz R, Valdés-Hernández MC, Dickie DA, Wardlaw J, Rueckert D.

Neuroimage Clin. 2017 Dec 20;17:918-934. doi: 10.1016/j.nicl.2017.12.022. eCollection 2018.


Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification.

Guo C, Niu K, Luo Y, Shi L, Wang Z, Zhao M, Wang D, Zhu W, Zhang H, Sun L.

Front Neurosci. 2019 Jul 10;13:679. doi: 10.3389/fnins.2019.00679. eCollection 2019.


Setting a gold standard for quantification of leukoaraiosis burden in patients with ischemic stroke: the Atherosclerosis Risk in Communities Study.

Rost NS, Sadaghiani S, Biffi A, Fitzpatrick KM, Cloonan L, Rosand J, Shibata DK, Mosley TH Jr.

J Neurosci Methods. 2014 Jan 15;221:196-201.


Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features.

Rincón M, Díaz-López E, Selnes P, Vegge K, Altmann M, Fladby T, Bjørnerud A.

Neuroinformatics. 2017 Jul;15(3):231-245. doi: 10.1007/s12021-017-9328-y.


Spatial Signature of White Matter Hyperintensities in Stroke Patients.

Schirmer MD, Giese AK, Fotiadis P, Etherton MR, Cloonan L, Viswanathan A, Greenberg SM, Wu O, Rost NS.

Front Neurol. 2019 Mar 19;10:208. doi: 10.3389/fneur.2019.00208. eCollection 2019.


UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

Jiang J, Liu T, Zhu W, Koncz R, Liu H, Lee T, Sachdev PS, Wen W.

Neuroimage. 2018 Jul 1;174:539-549. doi: 10.1016/j.neuroimage.2018.03.050. Epub 2018 Mar 22.


Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR.

Zhong Y, Utriainen D, Wang Y, Kang Y, Haacke EM.

Int J Biomed Imaging. 2014;2014:239123. doi: 10.1155/2014/239123. Epub 2014 Jul 22.


Automated quantification of white matter disease extent at 3 T: comparison with volumetric readings.

Hulsey KM, Gupta M, King KS, Peshock RM, Whittemore AR, McColl RW.

J Magn Reson Imaging. 2012 Aug;36(2):305-11. doi: 10.1002/jmri.23659. Epub 2012 Apr 19.


White matter hyperintensities segmentation: a new semi-automated method.

Iorio M, Spalletta G, Chiapponi C, Luccichenti G, Cacciari C, Orfei MD, Caltagirone C, Piras F.

Front Aging Neurosci. 2013 Dec 2;5:76. doi: 10.3389/fnagi.2013.00076. eCollection 2013.


Automatic segmentation and quantitative analysis of white matter hyperintensities on FLAIR images using trimmed-likelihood estimator.

Wang R, Li C, Wang J, Wei X, Li Y, Hui C, Zhu Y, Zhang S.

Acad Radiol. 2014 Dec;21(12):1512-23. doi: 10.1016/j.acra.2014.07.001. Epub 2014 Aug 28.


Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts.

Wang Y, Catindig JA, Hilal S, Soon HW, Ting E, Wong TY, Venketasubramanian N, Chen C, Qiu A.

Neuroimage. 2012 May 1;60(4):2379-88. doi: 10.1016/j.neuroimage.2012.02.034. Epub 2012 Feb 22.


On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology.

Valdés Hernández Mdel C, González-Castro V, Ghandour DT, Wang X, Doubal F, Muñoz Maniega S, Armitage PA, Wardlaw JM.

Neuroradiology. 2016 May;58(5):475-85. doi: 10.1007/s00234-016-1648-3. Epub 2016 Jan 30.


A fully automated method for quantifying and localizing white matter hyperintensities on MR images.

Wu M, Rosano C, Butters M, Whyte E, Nable M, Crooks R, Meltzer CC, Reynolds CF 3rd, Aizenstein HJ.

Psychiatry Res. 2006 Dec 1;148(2-3):133-42. Epub 2006 Nov 13.


White matter hyperintensity burden in patients with ischemic stroke treated with thrombectomy.

Boulouis G, Bricout N, Benhassen W, Ferrigno M, Turc G, Bretzner M, Benzakoun J, Seners P, Personnic T, Legrand L, Trystram D, Rodriguez-Regent C, Charidimou A, Rost NS, Bracard S, Cordonnier C, Oppenheim C, Naggara O, Henon H.

Neurology. 2019 Oct 15;93(16):e1498-e1506. doi: 10.1212/WNL.0000000000008317. Epub 2019 Sep 13.


Gray matter reduction is correlated with white matter hyperintensity volume: a voxel-based morphometric study in a large epidemiological sample.

Wen W, Sachdev PS, Chen X, Anstey K.

Neuroimage. 2006 Feb 15;29(4):1031-9. Epub 2005 Oct 25.


BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

Griffanti L, Zamboni G, Khan A, Li L, Bonifacio G, Sundaresan V, Schulz UG, Kuker W, Battaglini M, Rothwell PM, Jenkinson M.

Neuroimage. 2016 Nov 1;141:191-205. doi: 10.1016/j.neuroimage.2016.07.018. Epub 2016 Jul 9.


Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M.

PLoS One. 2012;7(11):e48953. doi: 10.1371/journal.pone.0048953. Epub 2012 Nov 12.


Validation and Optimization of BIANCA for the Segmentation of Extensive White Matter Hyperintensities.

Ling Y, Jouvent E, Cousyn L, Chabriat H, De Guio F.

Neuroinformatics. 2018 Apr;16(2):269-281. doi: 10.1007/s12021-018-9372-2.


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