Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies
Precise detection and quantification of white matter hyperintensities (WMH) observed in T2‐weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age‐related neurological disorders such as Alzheimer's disease (AD). This is m...
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| Published in | Human brain mapping Vol. 35; no. 8; pp. 4219 - 4235 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
Blackwell Publishing Ltd
01.08.2014
Wiley-Liss John Wiley & Sons, Inc John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.22472 |
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| Abstract | Precise detection and quantification of white matter hyperintensities (WMH) observed in T2‐weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age‐related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co‐morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle‐aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Hum Brain Mapp 35:4219–4235, 2014. © 2014 Wiley Periodicals, Inc. |
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| AbstractList | Precise detection and quantification of white matter hyperintensities (WMH) observed in T2‐weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age‐related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co‐morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle‐aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Hum Brain Mapp 35:4219–4235, 2014. © 2014 Wiley Periodicals, Inc. Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies.Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Hum Brain Mapp 35:4219-4235, 2014. © 2014 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT] |
| Author | Carlsson, Cynthia M. Bendlin, Barbara B. Ithapu, Vamsi Lindner, Christopher Johnson, Sterling C. Hinrichs, Chris Austin, Benjamin P. Singh, Vikas |
| AuthorAffiliation | 3 Department of Biostatistics and Medical Informatics University of Wisconsin‐Madison Madison Wisconsin 1 Department of Computer Sciences University of Wisconsin‐Madison Madison Wisconsin 5 Department of Electrical and Computer Engineering University of Wisconsin‐Madison Madison Wisconsin 6 William S. Middleton Memorial Veterans Hospital Madison Wisconsin 2 Wisconsin Alzheimer's Disease Research Center Madison Wisconsin 4 Department of Medicine University of Wisconsin‐Madison Madison Wisconsin |
| AuthorAffiliation_xml | – name: 5 Department of Electrical and Computer Engineering University of Wisconsin‐Madison Madison Wisconsin – name: 3 Department of Biostatistics and Medical Informatics University of Wisconsin‐Madison Madison Wisconsin – name: 2 Wisconsin Alzheimer's Disease Research Center Madison Wisconsin – name: 1 Department of Computer Sciences University of Wisconsin‐Madison Madison Wisconsin – name: 4 Department of Medicine University of Wisconsin‐Madison Madison Wisconsin – name: 6 William S. Middleton Memorial Veterans Hospital Madison Wisconsin |
| Author_xml | – sequence: 1 givenname: Vamsi surname: Ithapu fullname: Ithapu, Vamsi email: ithapu@wisc.edu organization: Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin – sequence: 2 givenname: Vikas surname: Singh fullname: Singh, Vikas organization: Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin – sequence: 3 givenname: Christopher surname: Lindner fullname: Lindner, Christopher organization: Department of Computer Sciences, University of Wisconsin-Madison, Wisconsin, Madison – sequence: 4 givenname: Benjamin P. surname: Austin fullname: Austin, Benjamin P. organization: Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin – sequence: 5 givenname: Chris surname: Hinrichs fullname: Hinrichs, Chris organization: Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Wisconsin, Madison – sequence: 6 givenname: Cynthia M. surname: Carlsson fullname: Carlsson, Cynthia M. organization: Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin – sequence: 7 givenname: Barbara B. surname: Bendlin fullname: Bendlin, Barbara B. organization: Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin – sequence: 8 givenname: Sterling C. surname: Johnson fullname: Johnson, Sterling C. organization: Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin |
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| Keywords | Nervous system diseases Senescence support vector machines Radiodiagnosis Segmentation Image processing Alzheimer disease white matter hyperintensities random forests White matter Cerebral disorder Central nervous system disease Risk factor Degenerative disease Vector Hyperintensity segmentation |
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McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Theis B, Weintraub S, Phelps CH (2011): The diagnosis of dementia due to Alzheimers disease: Recommendations from the National Institute on Aging-Alzheimers Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7:263-269. Brickman AM, Siedlecki KL, Muraskin J, Manly JJ, Luchsinger JA, Yeung LK, Brown TR, DeCarli C, Stern Y (2011): White matter hyperintensities and cognition: Testing the reserve hypothesis. Neurobiol Aging 32:1588-1598. Shi J, Malik J (2000): Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888-905. Breiman L. (2001): Random forests. Mach Learn 45:5-32 2004; 21 2009; 45 2005a; 36 1987; 3 2011 2010 2000; 22 2008; 39 2010; 341 1999; 67 1997 2008; 15 2008 2011; 33 2011; 32 2011; 54 2011; 76 2011; 57 2012; 18 1999; 2 2011; 59 2001; 23 2001; 45 2005; 28 2012; 34 2010; 41 2011; 7 2012; 30 2003; 34 2001; 43 2010; 67 1995; 20 2009; 11 2006; 63 2009; 73 2001 2006; 67 2005; 9 2002; 24 2006; 28 2011; 68 2008; 65 2002; 109 2012; 69 2005b; 26 2010; 30 2010; 50 e_1_2_7_5_1 e_1_2_7_3_1 Barber R (e_1_2_7_7_1) 1999; 67 e_1_2_7_9_1 e_1_2_7_19_1 e_1_2_7_17_1 e_1_2_7_15_1 e_1_2_7_41_1 e_1_2_7_13_1 e_1_2_7_43_1 e_1_2_7_11_1 e_1_2_7_47_1 Forsyth DA (e_1_2_7_24_1) 2011 e_1_2_7_26_1 e_1_2_7_49_1 e_1_2_7_28_1 e_1_2_7_50_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_37_1 e_1_2_7_39_1 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_8_1 e_1_2_7_18_1 e_1_2_7_16_1 e_1_2_7_40_1 e_1_2_7_2_1 e_1_2_7_14_1 e_1_2_7_42_1 e_1_2_7_12_1 e_1_2_7_44_1 e_1_2_7_10_1 e_1_2_7_46_1 e_1_2_7_48_1 e_1_2_7_27_1 e_1_2_7_29_1 e_1_2_7_30_1 e_1_2_7_32_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_38_1 Schölkopf B (e_1_2_7_45_1) 2001 20733228 - IEEE Trans Pattern Anal Mach Intell. 2011 May;33(5):898-916 22945686 - Arch Neurol. 2012 Dec;69(12):1621-7 15576652 - Stroke. 2005 Jan;36(1):50-5 21518999 - Neurology. 2011 Apr 26;76(17):1492-9 15006671 - Neuroimage. 2004 Mar;21(3):1037-44 22390883 - J Int Neuropsychol Soc. 2012 May;18(3):414-27 17190943 - Neurology. 2006 Dec 26;67(12):2192-8 21497655 - Neuroimage. 2011 Jul 15;57(2):378-90 18280928 - Acad Radiol. 2008 Mar;15(3):300-13 12111471 - J Neural Transm (Vienna). 2002 May;109(5-6):813-36 22159052 - Arch Neurol. 2011 Dec;68(12):1514-20 19585953 - Dialogues Clin Neurosci. 2009;11(2):181-90 15693993 - Crit Care. 2005 Feb;9(1):112-8 22700749 - AJNR Am J Neuroradiol. 2013 Jan;34(1):54-61 21514250 - Alzheimers Dement. 2011 May;7(3):263-9 16129626 - Neuroimage. 2005 Nov 15;28(3):607-17 17063682 - IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83 18195145 - Arch Neurol. 2008 Jan;65(1):94-100 20114082 - Neuroimage. 2010 May 1;50(4):1427-37 21514249 - Alzheimers Dement. 2011 May;7(3):270-9 18006334 - Neuroimage. 2008 Feb 1;39(3):987-96 12574526 - Stroke. 2003 Feb;34(2):330-2 20660506 - BMJ. 2010;341:c3666 20849961 - Neuroimage. 2011 Jan 15;54(2):963-73 21060014 - Arch Neurol. 2010 Nov;67(11):1370-8 19515125 - Cephalalgia. 2010 Feb;30(2):129-36 15653178 - Neurobiol Aging. 2005 Apr;26(4):491-510 10369824 - J Neurol Neurosurg Psychiatry. 1999 Jul;67(1):66-72 22119648 - Neuroimage. 2012 Feb 15;59(4):3774-83 19667320 - Neurology. 2009 Aug 11;73(6):450-6 20167919 - Stroke. 2010 Apr;41(4):600-6 22578927 - Magn Reson Imaging. 2012 Jul;30(6):807-23 19926168 - Neurobiol Aging. 2011 Sep;32(9):1588-98 12690219 - Stroke. 2003 May;34(5):1126-9 16476813 - Arch Neurol. 2006 Feb;63(2):246-50 19344687 - Neuroimage. 2009 May 1;45(4):1151-61 |
| References_xml | – reference: Maillard P, Carmichael O, Harvey D, Fletcher E, Reed B, Mungas D, DeCarli C (2012): FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol 34:54-61. – reference: Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011): Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 57:378-390. – reference: Keihaninejad S, Heckemann RA, Fagiolo G, Symms MR, Hajnal JV, Hammers A (2010): A robust method to estimate the intracranial volume across MRI field strengths (1.5 T and 3T). Neuroimage 50:1427-1437. – reference: Smith EE, Salat DH, Jeng J, McCreary CR, Fischl B, Schmahmann JD, Dickerson BC, Viswanathan A, Albert MS, Blacker D, Greenberg SM (2011): Correlations between MRI white matter lesion location and executive function and episodic memory. Neurology 76:1492-1499. – reference: Arbelaez P, Maire M, Fowlkes C, Malik J (2011): Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33:898-916. – reference: Goldberg MP, Ransom BR (2003): New light on white matter. Stroke 34:330-332. – reference: Boykov Y, Veksler O, Zabih R (2001): Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23:1222-1239. – reference: Brickman AM, Provenzano FA, Muraskin J, Manly JJ, Blum S, Apa Z, Stern Y, Brown TR, Luchsinger JA, Mayeux R (2012): Regional white matter hyperintensity volume, not hippocampal atrophy, predicts incident Alzheimer disease in the community. Arch Neurol 69:1621-1627. – reference: Schölkopf B, Smola AJ (2001): Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press. – reference: Carmichael O, Schwarz C, Drucker D, Fletcher E, Harvey D, Beckett L, Jack CR Jr, Weiner M, DeCarli C; Alzheimer's Disease Neuroimaging Initiative (2010): Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. Arch Neurol 67:1370. – reference: Barber R, Scheltens P, Gholkar A, Ballard C, McKeith I, Ince P, Perry R, OBrien J (1999): White matter lesions on magnetic resonance imaging in dementia with lewy bodies, alzheimers disease, vascular dementia, and normal aging. J Neurol 67:66-72. – reference: Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J (2004): Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 21:1037-1044. – reference: de Boer R, Vrooman HA, van der Lijn F, Vernooij MW, Ikram MA, van der Lugt A, Breteler MMB, Niessen WJ (2009): White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 45:1151-1161. – reference: Lee J, Haralick R, Shapiro L (1987): Morphologic edge detection. IEEE Robot Automation 3:142-156,. – reference: Vermeer SE, Hollander M, van Dijk EJ, Hofman A, Koudstaal PJ, Breteler M (2003): Silent brain infarcts and white matter lesions increase stroke risk in the general population. Stroke 34:1126-1129. – reference: Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH (2011): The diagnosis of mild cognitive impairment due to Alzheimers disease: Recommendations from the National Institute on Aging-Alzheimers Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7:270-279. – reference: Comaniciu D, Meer P (2002): Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603-619. – reference: Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly-Hayes M, Romero JR, Kase CS, Wolf PA, Seshadri S (2010): Association of MRI markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality: The Framingham offspring study. Stroke 41:600-606. – reference: Shi J, Malik J (2000): Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888-905. – reference: Kruit MC, Van Buchem MA, Launer LJ, Terwindt GM, Ferrari MD (2010): Migraine is associated with an increased risk of deep white matter lesions, subclinical posterior circulation infarcts and brain iron accumulation: the population-based MRI CAMERA study. Cephalalgia 30:129-136. – reference: McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Theis B, Weintraub S, Phelps CH (2011): The diagnosis of dementia due to Alzheimers disease: Recommendations from the National Institute on Aging-Alzheimers Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7:263-269. – reference: DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, Beiser A, DAgostino R, Wolf PA (2005b): Measures of brain morphology and infarction in the Framingham Heart Study: Establishing what is normal. Neurobiol Aging 26:491-510. – reference: Meier IB, Manly JJ, Provenzano FA, Louie KS, Wasserman BT, Griffth EY, Hector JT, Allocco E, Brickman AM (2012): White matter predictors of cognitive functioning in older adults. J Int Neuropsychol Soc 18:414. – reference: Au R, Massaro JM, Wolf PA, Young ME, Beiser A, Seshadri S, D'Agostino RB, DeCarli C (2006): Association of white matter hyperintensity volume with decreased cognitive functioning: The Framingham heart study. Arch Neurol 63:246. – reference: Breiman L. (2001): Random forests. Mach Learn 45:5-32 – reference: Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan RN, Davatzikos C (2008): Computerassisted segmentation of white matter lesions in 3D MR images, using support vector machine. Acad Radiol 15:300. – reference: Ramirez J, Gibson E, Quddus A, Lobaugh NJ, Feinstein A, Levine B, Scott CJM, Levy-Cooperman N, Gao FQ, Black SE (2011): Lesion explorer: A comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue. Neuroimage 54:963-973. – reference: Brickman AM, Muraskin J, Zimmerman ME (2009): Structural neuroimaging in Alzheimer's disease: do white matter hyperintensities matter? Dialogues Clin Neurosci 11:181. – reference: Cortes C, Vapnik V (1995): Support vector networks. Machine Learn 20:273-297. – reference: Filippi M, Rocca MA, De Stefano N, Enzinger C, Fisher E, Horsfield MA, Inglese M, Pelletier D, Comi G (2011): Magnetic resonance techniques in multiple sclerosis: The present and the future. Arch Neurol 68:1514. – reference: Brickman AM, Siedlecki KL, Muraskin J, Manly JJ, Luchsinger JA, Yeung LK, Brown TR, DeCarli C, Stern Y (2011): White matter hyperintensities and cognition: Testing the reserve hypothesis. Neurobiol Aging 32:1588-1598. – reference: Debette S, Markus HS (2010): The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. Br Med J 341:3666. – reference: Grady L (2006): Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28:1768-1783. – reference: Manning CD, Raghavan P, Schütze H (2008): Introduction to Information Retrieval. New York, NY, USA: Cambridge University Press. – reference: Ong KH, Ramachandram D, Mandava R, Shuaib IL (2012): Automatic white matter lesion segmentation using an adaptive outlier detection method. Magn Reson Imaging 30:807-823. – reference: Leung T, Malik J (2001): Representing and recognizing the visual appearance of materials using threedimensional textons. Int J Comput Vis 43:29-44. – reference: DeCarli C, Fletcher E, Ramey V, Harvey D, Jagust WJ (2005a): Anatomical mapping of white matter hyperintensities (wmh) exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke 36:50-55. – reference: Schmidt P, Gaser C, Arsic M, Buck D, Forschler A, Berthele A, Hoshi M, Ilg R, Schmid VJ, Zimmer C, Hemmer B, Muhlau M (2011): An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59:3774-3783. – reference: Yoshita M, Fletcher E, Harvey D, Ortega M, Martinez O, Mungas DM, Reed BR, DeCarli CS (2006): Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology 67:2192-2198. – reference: Jellinger KA (2002): Alzheimer disease and cerebrovascular pathology: An update. J Neural Transm 109:813-836. – reference: Kruggel F, Paul JS, Gertz HJ (2008): Texture-based segmentation of diffuse lesions of the brain's white matter. Neuroimage 39:987-996. – reference: Smith EE, Egorova S, Blacker D, Killiany RJ, Muzikansky A, Dickerson BC, Tanzi RE, Albert MS, Greenberg SM, Guttmann CRG (2008): Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia. Arch Neurol 65:94-100. – reference: Forsyth DA, Ponce J (2011): Computer Vision: A Modern Approach. NJ, USA: Prentice Hall. – reference: Admiraal-Behloul F, Van Den Heuvel DMJ, Olofsen H, Van Osch MJP, Van der Grond J, Van Buchem MA, Reiber JHC (2005): Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. Neuroimage 28:607-617. – reference: Luchsinger JA, Brickman AM, Reitz C, Cho SJ, Schupf N, Manly JJ, Tang MX, Small SA, Mayeux R, DeCarli C, Brown TR (2009): Subclinical cerebrovascular disease in mild cognitive impairment. Neurology 73:450-456. – reference: Bewick V, Cheek L, Ball J (2005): Statistics review 14: Logistic regression. 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| SubjectTerms | Aged Aged, 80 and over Aging - pathology Alzheimer Disease - pathology Artificial Intelligence Biological and medical sciences Brain - pathology Cognitive Dysfunction - pathology Cohort Studies Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases Female Humans Investigative techniques, diagnostic techniques (general aspects) Linear Models Magnetic Resonance Imaging - methods Male Medical sciences Middle Aged Nervous system Neurology Radiodiagnosis. Nmr imagery. Nmr spectrometry random forests Risk segmentation Signal Processing, Computer-Assisted Support Vector Machine support vector machines White Matter - pathology white matter hyperintensities |
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| Title | Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies |
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