Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review

Neuroinformatics

Volumn 13, Issue 3, 2015, Pages 261-276

  Caligiuri, Maria Eugenia ^a   Perrotta, Paolo ^a   Augimeri, Antonio ^a   Rocca, Federico ^a   Quattrone, Aldo ^a,b   Cherubini, Andrea ^a  

^a Institute of Molecular Bioimaging and Physiology   (Italy)

^b UNIVERSITY MAGNA GRAECIA OF CATANZARO   (Italy)

Author keywords

Aging; Automated segmentation; Brain MRI; Small vessel disease; Vascular pathology; White matter hyperintensities

Indexed keywords

AGING; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BRAIN; HUMAN; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; VASCULARIZATION; WHITE MATTER;

AGING; ALGORITHMS; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; WHITE MATTER;

EID: 84931265689     PISSN: 15392791     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12021-015-9260-y     Document Type: Review

References (81)

1. Admiraal-Behloul, F., van den Heuvel, D. M. J., Olofsen, H., van Osch, M. J. P., van der Grond, J., van Buchem, M., & Reiber, J. H. C. (2005). Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage, 28(3), 607–17. doi:10.1016/j.neuroimage.2005.06.061.
2. Anbeek, P., Vincken, K. L., van Osch, M. J. P., Bisschops, R. H. C., & van der Grond, J. (2004). Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage, 21(3), 1037–44. doi:10.1016/j.neuroimage.2003.10.012.
3. Anitha, M., Selvy, P. T., & Palanisamy, V. (2012). WML detection of brain images using fuzzy and possibilistic approach in feature space, WSEAS TRANSACTIONS on COMPUTERS, E-ISSN, 2224–2872.
4. Bastianello, S., Bozzao, A., Paolillo, A., Giugni, E., Gasperini, C., Koudriavtseva, T., … Bozzao, L. (1997). Fast spin-echo and fast fluid-attenuated inversion-recovery versus conventional spin-echo sequences for MR quantification of multiple sclerosis lesions. American Journal of Neuroradiology, 18 (4), 699–704.
5. Bastos Leite, A. J., van Straaten, E. C., Scheltens, P., Lycklama, G., & Barkhof, F. (2004). Thalamic lesions in vascular dementia: low sensitivity of fluid-attenuated inversion recovery (FLAIR) imaging. Stroke, 35(2), 415–9.
6. Beare, R., Srikanth, V., Chen, J., Phan, T. G., Stapleton, J., Lipshut, R., & Reutens, D. (2009). Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities. NeuroImage, 47(1), 199–203. doi:10.1016/j.neuroimage.2009.03.055.
7. Brickman, A. M., Sneed, J. R., Provenzano, F. a, Garcon, E., Johnert, L., Muraskin, J., … Roose, S. P. (2011). Quantitative approaches for assessment of white matter hyperintensities in elderly populations. Psychiatry Research, 193 (2), 101–6. doi:10.1016/j.pscychresns.2011.03.007.
8. Cao, J. (1999). The size of the connected components of excursion sets of χ 2, t and F fields. Advances in Applied Probability, 579–595.
9. Dade, L. A., Gao, F. Q., Kovacevic, N., Roy, P., Rockel, C., O’Toole, C. M., … Black, S. E. (2004). Semiautomatic brain region extraction: a method of parcellating brain regions from structural magnetic resonance images. NeuroImage, 22 (4), 1492–502. doi:10.1016/j.neuroimage.2004.03.023.
10. de Boer, R., Vrooman, H. a, van der Lijn, F., Vernooij, M. W., Ikram, M. A., van der Lugt, A., … Niessen, W. J. (2009). White matter lesion extension to automatic brain tissue segmentation on MRI. NeuroImage, 45 (4), 1151–61. doi:10.1016/j.neuroimage.2009.01.011.
11. Debette, S., & Markus, H. S. (2010). The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ, 341, c3666. doi:10.1136/bmj.c3666.
12. DeCarli, C., Murphy, D. G., Tranh, M., Grady, C. L., Haxby, J. V, Gillette, J. a, … Rapoport, S. I. (1995). The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology, 45 (11), 2077–84.
13. Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297–302.
14. Duda, R. O., Hart, P. E., & Stork, D. G. (1973). Pattern classification. New York: Wiley.
15. Dyrby, T. B., Rostrup, E., Baaré, W. F. C., van Straaten, E. C. W., Barkhof, F., Vrenken, H., … Waldemar, G. (2008). Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage, 41 (2), 335–45. doi:10.1016/j.neuroimage.2008.02.024.
16. Ebihara, M., Mahara, H., Sakurai, T., Nomura, A., & Miike, H. (2003). Image processing by a discrete reaction–diffusion system. Proceeding of Visualization, Imaging, and Image Processing, 396, 145–150.
17. Filippi, M., Rovaris, M., Campi, A., Pereira, C., & Comi, G. (1996a). Semi-automated thresholding technique for measuring lesion volumes in multiple sclerosis: effects of the change of the threshold on the computed lesion loads. Acta Neurologica Scandinavica, 93(1), 30–4.
18. Filippi, M., Baratti, C., Yousry, T., Horsfield, M. A., Mammi, S., Becker, C., … & Comi, G. (1996b). Quantitative assessment of MRI lesion load in multiple sclerosis A comparison of conventional spin-echo with fast fluid attenuated inversion recovery. Brain, 119 (4), 1349–1355.
19. Filippi, M., Rocca, M. A., Gasperini, C., Sormani, M. P., Bastianello, S., Horsfield, M. A., … Comi, G. (1999). Interscanner variation in brain MR lesion load measurements in multiple sclerosis using conventional spin-echo, rapid relaxation-enhanced, and fast-FLAIR sequences. American Journal of Neuroradiology, 20 (1), 133–7.
20. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33 (3), 341–55.
21. Freund, Y., Schapire, R., & Abe, N. (1999). A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14(771–780), 1612.
22. Gao, F. Q., Swartz, R. H., Scheltens, P., et al. (2011). Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study. Journal of Alzheimer’s Disease, 26(Suppl 3), 379–88. doi:10.3233/JAD-2011-0058.
23. García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D. L., & Collins, D. L. (2013). Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Medical Image Analysis, 17(1), 1–18. doi:10.1016/j.media.2012.09.004.
24. Gawne-Cain, M. L., O’Riordan, J. I., Coles, A., Newell, B., Thompson, A. J., & Miller, D. H. (1998). MRI lesion volume measurement in multiple sclerosis and its correlation with disability: a comparison of fast fluid attenuated inversion recovery (fFLAIR) and spin echo sequences. Journal of Neurology Neurosurgery and Psychiatry, 64(2), 197–203.
25. Gibson, E., Gao, F., Black, S. E., & Lobaugh, N. J. (2010). Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T. Journal of Magnetic Resonance Imaging, 31(6), 1311–22. doi:10.1002/jmri.22004.
26. Goldberg-Zimring, D., Achiron, A., Miron, S., Faibel, M., & Azhari, H. (1998). Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magnetic Resonance Imaging, 16(3), 311–8.
27. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing.
28. Grimaud, J., Lai, M., Thorpe, J., Adeleine, P., Wang, L., Barker, G. J., … Miller, D. H. (1996). Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. Magnetic resonance imaging, 14 (5), 495–505.
29. Gurol, M. E., Irizarry, M. C., Smith, E. E., Raju, S., Diaz-Arrastia, R., Bottiglieri, T., … Greenberg, S. M. (2006). Plasma beta-amyloid and white matter lesions in AD, MCI, and cerebral amyloid angiopathy. Neurology, 66 (1), 23–9. doi:10.1212/01.wnl.0000191403.95453.6a.
30. Haller, S., Kövari, E., Herrmann, F. R., Cuvinciuc, V., Tomm, A. M., Zulian, G. B., … & Bouras, C. (2013). Do brain T2/FLAIR white matter hyperintensities correspond to myelin loss in normal aging? A radiologic-neuropathologic correlation study. Acta Neuropathologica Commun, 1, 14.
31. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. Intelligent Systems and their Applications IEEE, 13(4), 18–28. doi:10.1109/5254.708428.
32. Held, K., Kops, E. R., Krause, B. J., Wells, W. M. I., Kikinis, I. I. R., & Muller-Gartner, H. W. (1997). Markov random field segmentation of brain MR images. Medical Imaging, IEEE Transactions on, 16(6), 878–886.
33. Herskovits, E. H., Bryan, R. N., & Yang, F. (2008). Automated Bayesian segmentation of microvascular white-matter lesions in the ACCORD-MIND study. Advances in Medical Sciences, 53(2), 182–90. doi:10.2478/v10039-008-0039-3.
34. Itti, L., Chang, L., & Ernst, T. (2001). Segmentation of progressive multifocal leukoencephalopathy lesions in fluid-attenuated inversion recovery magnetic resonance imaging. Journal of Neuroimaging, 11(4), 412–417.
35. Jack, C. R., Brien, P. C. O., Rettman, D. W., Shiung, M. M., Xu, Y., Muthupillai, R., … Erickson, B. J. (2001). FLAIR Histogram Segmentation for Measurement of Leukoaraiosis Volume. Journal of Magnetic Resonance Imaging, 14 (6), 668–676. doi:10.1002/jmri.10011.
36. Jeon, S., Yoon, U., Park, J. S., Seo, S. W., Kim, J. H., Kim, S. T., … & Lee, J. M. (2011). Fully automated pipeline for quantification and localization of white matter hyperintensity in brain magnetic resonance image. International Journal of Imaging Systems and Technology, 21 (2), 193–200. doi:10.1002/ima.20277.
37. Ji, S., Ye, C., Li, F., Sun, W., Zhang, J., Huang, Y., & Fang, J. (2013). Automatic segmentation of white matter hyperintensities by an extended FitzHugh & Nagumo reaction diffusion model. Journal of Magnetic Resonance Imaging, 37(2), 343–50. doi:10.1002/jmri.23836.
38. Kawata, Y., Arimura, H., Yamashita, Y., Magome, T., Ohki, M., Toyofuku, F., … & Tsuchiya, K. (2010). Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging. Computerized Medical Imaging and Graphics, 34 (5), 370–376. doi:10.1016/j.compmedimag.2009.12.014.
39. Khademi, A., Venetsanopoulos, A., & Moody, A. R. (2012). Robust white matter lesion segmentation. IEEE Transactions on Biomedical Engineering, 59(3), 860–871.
40. Kim, K. W., MacFall, J. R., & Payne, M. E. (2008). Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biological Psychiatry, 64(4), 273–80. doi:10.1016/j.biopsych.2008.03.024.
41. Klöppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T. N., … Ronneberger, O. (2011). A comparison of different automated methods for the detection of white matter lesions in MRI data. NeuroImage, 57 (2), 416–22. doi:10.1016/j.neuroimage.2011.04.053.
42. Kovacevic, N., Lobaugh, N. J., Bronskill, M. J., Levine, B., Feinstein, A., & Black, S. E. (2002). A robust method for extraction and automatic segmentation of brain images. NeuroImage, 17(3), 1087–100.
43. Kruggel, F., Paul, J. S., & Gertz, H.-J. (2008). Texture-based segmentation of diffuse lesions of the brain’s white matter. NeuroImage, 39(3), 987–96. doi:10.1016/j.neuroimage.2007.09.058.
44. Lao, Z., Shen, D., Liu, D., Jawad, A. F., Melhem, E. R., Launer, L. J., … Davatzikos, C. (2008). Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Academic Radiology, 15 (3), 300–13. doi:10.1016/j.acra.2007.10.012.
45. Lin, Y. (2002). Support vector machines and the Bayes rule in classification. Data Mining and Knowledge Discovery, 6(3), 259–275.
46. Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J. C., Quiles, A., … & Rovira, À. (2012). Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Information Sciences, 186 (1), 164–185. doi:10.1016/j.ins.2011.10.011.
47. Maillard, P., Delcroix, N., Crivello, F., Dufouil, C., Gicquel, S., Joliot, M., … Mazoyer, B. (2008). An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases. Neuroradiology, 50 (1), 31–42. doi:10.1007/s00234-007-0312-3.
48. Maldjian, J., Whitlow, C. T., Saha, B. N., Kota, G., Vandergriff, C., Davenport, E. M., … Bowden, D. W. (2013). Automated white matter total lesion volume segmentation in diabetes. American Journal of Neuroradiology, 34 (12), 2265–70. doi:10.3174/ajnr.A3590.
49. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man–machine Studies, 7(1), 1–13. doi:10.1016/S0020-7373(75)80002-2.
50. Mäntylä, R., Erkinjuntti, T., Salonen, O., Aronen, H. J., Peltonen, T., Pohjasvaara, T., & Standertskjöld-Nordenstam, C. G. (1997). Variable agreement between visual rating scales for white matter hyperintensities on MRI. comparison of 13 rating scales in a poststroke cohort. Stroke, 28(8), 1614–23.
51. Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
52. Ong, K. H., Ramachandram, D., Mandava, R., & Shuaib, I. L. (2012). Automatic white matter lesion segmentation using an adaptive outlier detection method. Magnetic Resonance Imaging, 30(6), 807–23. doi:10.1016/j.mri.2012.01.007.
53. Payne, M. E., Fetzer, D. L., MacFall, J. R., Provenzale, J. M., Byrum, C. E., & Krishnan, K. R. R. (2002). Development of a semi-automated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatry Research, 115(1–2), 63–77.
54. Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 12(7), 629–639.
55. Prins, N. D., van Straaten, E. C., van Dijk, E. J., et al. (2004). Measuring progression of cerebral white matter lesions on MRI: visual ratings and volumetrics. Neurology, 62(9), 1533–9.
56. Ramirez, J., Gibson, E., Quddus, a, Lobaugh, N. J., Feinstein, a, Levine, B., … Black, S. E. (2011). Lesion Explorer: a comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue. NeuroImage, 54 (2), 963–73. doi:10.1016/j.neuroimage.2010.09.013.
57. Rangayyan, R. M. (2004). Biomedical image analysis (pp. 583–796). London: CRC press.
58. Rovaris, M., Comi, G., Rocca, M. A., Cercignani, M., Colombo, B., Santuccio, G., & Filippi, M. (1999). Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. American Journal of Neuroradiology, 20(5), 813–20.
59. Samaille, T., Fillon, L., Cuingnet, R., Jouvent, E., Chabriat, H., Dormont, D., … Chupin, M. (2012). Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PloS one, 7 (11), e48953. doi:10.1371/journal.pone.0048953.
60. Scheltens, P., Erkinjunti, T., Leys, D., Wahlund, L. O., Inzitari, D., del Ser, T., … Pantoni, L. (1998). White matter changes on CT and MRI: an overview of visual rating scales. European Task Force on Age-Related White Matter Changes. European Neurology, 39 (2), 80–9.
61. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59 (4), 3774–83. doi:10.1016/j.neuroimage.2011.11.032.
62. Schwarz, C., Fletcher, E., DeCarli, C., & Carmichael, O. (2009, January). Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR. In Information processing in medical imaging (pp. 239–251). Springer Berlin Heidelberg.
63. Seghier, M. L., Ramlackhansingh, A., Crinion, J., Leff, A. P., & Price, C. J. (2008). Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage, 41(4), 1253–66. doi:10.1016/j.neuroimage.2008.03.028.
64. Sheline, Y. I., Price, J. L., Vaishnavi, S. N., Mintun, M. a, Barch, D. M., Epstein, A. a, … McKinstry, R. C. (2008). Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. The American journal of psychiatry, 165 (4), 524–32. doi:10.1176/appi.ajp.2007.07010175.
65. Shi, L., Wang, D., Liu, S., Pu, Y., Wang, Y., Chu, W. C. W., … Wang, Y. (2013). Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction. Journal of Neuroscience Methods, 213 (1), 138–46. doi:10.1016/j.jneumeth.2012.12.014.
66. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.
67. Simões, R., Mönninghoff, C., Dlugaj, M., Weimar, C., Wanke, I., van Cappellen van Walsum, A.-M., & Slump, C. (2013). Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images. Magnetic Resonance Imaging, 31(7), 1182–9. doi:10.1016/j.mri.2012.12.004.
68. Styner, M., Lee, J., Chin, B., Chin, M., Commowick, O., Tran, H., … & Warfield, S. (2008). 3D segmentation in the clinic: A grand challenge II: MS lesion segmentation. MIDAS Journal, 2008, 1–6.
69. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics IEEE Transactions on, 1, 116–132. doi:10.1109/TSMC.1985.6313399.
70. Tang, H., Dillenseger, J. L., Bao, X. D., & Luo, L. M. (2009). A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model. Computerized Medical Imaging and Graphics, 33(8), 644–650.
71. Valdés Hernández, M. D. C., Ferguson, K. J., Chappell, F. M., & Wardlaw, J. M. (2010). New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images. European Radiology, 20(7), 1684–1691. doi:10.1007/s00330-010-1718-6.
72. Valdés Hernández, M. D. C., Gallacher, P. J., Bastin, M. E., Royle, N. A., Maniega, S. M., Deary, I. J., & Wardlaw, J. M. (2012). Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques. Magnetic Resonance Imaging, 30(2), 222–229. doi:10.1016/j.mri.2011.09.016.
73. Valdés Hernández, M. D. C., Piper, R. J., Wang, X., Deary, I. J., & Wardlaw, J. M. (2013). Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review. Journal of Magnetic Resonance Imaging, 38(4), 774–85. doi:10.1002/jmri.24047. Epub 2013 Feb 25.
74. Van den Heuvel, D. M. J., ten Dam, V. H., de Craen, A. J. M., Admiraal-Behloul, F., van Es, A. C. G. M., Palm, W. M., … van Buchem, M. A. (2006). Measuring longitudinal white matter changes: comparison of a visual rating scale with a volumetric measurement. American Journal of Neuroradiology, 27 (4), 875–8.
75. Van Straaten, E. C. W., Fazekas, F., Rostrup, E., Scheltens, P., Schmidt, R., Pantoni, L., … Barkhof, F. (2006). Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke, 37 (3), 836–40. doi:10.1161/01.STR.0000202585.26325.74.
76. Wang, Y., Catindig, J. A., Hilal, S., Soon, H. W., Ting, E., Wong, T. Y., … Qiu, A. (2012). Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts. NeuroImage, 60 (4), 2379–88. doi:10.1016/j.neuroimage.2012.02.034.
77. Wardlaw, J. M., Smith, E. E., Biessels, G. J., Cordonnier, C., Fazekas, F., Frayne, R., … STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1) (2013). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurology, 12 (8), 822–38. doi: 10.1016/S1474-4422 (13)70124-8.
78. Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., … Aizenstein, H. J. (2006). A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Research, 148 (2–3), 133–42. doi:10.1016/j.pscychresns.2006.09.003.
79. Yang, F., Shan, Z. Y., & Kruggel, F. (2010). White matter lesion segmentation based on feature joint occurrence probability and χ2 random field theory from magnetic resonance (MR) images. Pattern Recognition Letters, 31(9), 781–790. doi:10.1016/j.patrec.2010.01.025.
80. Yoo, B. I., Lee, J. J., Han, J. W., Oh, S. Y., Lee, E. Y., MacFall, J. R., … Kim, K. W. (2014). Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance imaging. Neuroradiology, 56 (4), 265–81. doi:10.1007/s00234-014-1322-6.
81. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. doi:10.1109/42.906424.