ORIGINAL ARTICLES AUTOMATIC WHITE MATTER BRAIN LESIONS SEGMENTATION
Abstract
Quantitative characterization of brain lesions may not be only a diagnostic criteria, but also the parameter for dynamic evaluation of various CNS diseases progression. Manual segmentation is the most accurate method of measuring lesions volume, but this method is time- and labor-intensive. There are different methods of automatic lesions segmentation that use different algorithms and have varied accuracy of the results. In this paper we compare two fundamentally different approaches to the automatic lesions segmentation basis on the MRI images and evaluate the possibility of using these methods as an alternative to manual segmentation.
About the Authors
E. P. Magonov
N. P Bechtereva Institute of the Human Brain of the Russian Academy of Sciences; Russian-Finnish clinic «Scandinavia»
Russian Federation
Russian Federation
G. V. Kataeva
N. P Bechtereva Institute of the Human Brain of the Russian Academy of Sciences
Russian Federation
Russian Federation
T. N. Trofimova
Russian-Finnish clinic «Scandinavia»
Russian Federation
Russian Federation
References
1. Ormerod I. E. C. The role of NMR imaging in the assessment of multiple sclerosis and isolated neurological lesions / I. E. C. Ormerod, D. H. Miller, W. I. McDonald, E. P. G. H. DU Boulay, P. Rudge, B. E. Kendall, I. F. Moseley, G. Johnson, P. S. Tofts, A. M. Halliday, A. M. Bronstein, F. Scaravilli, A. E. Harding, D. Barnes, K. J. Zilkha // Brain.- 1987.- Vol. 6, № 110.- С. 1579-1616
2. Bakshi R. Predicting clinical progression in multiple sclerosis with the magnetic resonance disease severity scale / R. Bakshi, M. Neema, B. C. Healy, Z. Liptak, R. A. Betensky, G. J. Buckle, S. A.Gauthier, J. Stankiewicz, D. Meier, S. Egorova, A. Arora, Z. D. Guss, B. Glanz, S. J. Khoury, C. R. Guttmann, H. L. Weiner // Archives of neurology.- 2008.- Vol. 11, № 65.- С. 1449-1453
3. De Groot J. C. Periventricular cerebral white matter lesions predict rate of cognitive decline / J. C. De Groot, F. E. De Leeuw, M. Oudkerk, J. Van Gijn, A. Hofman, J. Jolles, M. M. Breteler // Annals of Neurology.- 2002.- С. 335-341
4. Vermeer S. E. Silent brain infarcts and white matter lesions increase stroke risk in the general population: the Rotterdam Scan Study / S. E. Vermeer, M. Hollander, E. J. van Dijk, A. Hofmann, P J. Koudstaal, M. M. Breteler // 2003.- Vol. 5, № 34.- С. 1126-1129
5. De Boer R. White matter lesion extension to automatic brain tissue segmentation on MRI / R. de Boer, H. A. Vrooman, F. van der Lijn, M. W Vernooij, M. A. Ikram, A. van der Lugt, M. M. Breteler, W J. Niessen // Neuroimage.- 2009.- Vol. 4, № 45.- С. 1151-1161
6. Alfano B. Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis / B. Alfano, A. Brunetti, M. Larobina, M. Quarantelli, E. Tedeschi, A. Ciarmiello, E. M. Covelli, M. Salvatore // 2000.- Vol. 6, № 12.- С. 799-807.
7. Магонов Е. П. Автоматическая сегментация МРТ-изображений головного мозга: методы и программное обеспечение / Е. П. Магонов, Т. Н. Трофимова // Лучевая диагностика и терапия.- 2012.- № 3 (2).- С. 35-40.
8. Lao Z. Q. Computer-assisted segmentation of white matter lesions in 3D MR images, using support vector machine / Z. Q. Lao, D. G. Shen, D. F. Liu, A. F. Jawad, E. R. Melhem, L. J. Launer, R. N. Bryan, C. Davatzikos // Academic Radiology.- 2008.- Vol. 3, № 15.- С. 300-313.
Review
For citations:
Magonov E.P., Kataeva G.V., Trofimova T.N. ORIGINAL ARTICLES AUTOMATIC WHITE MATTER BRAIN LESIONS SEGMENTATION. Diagnostic radiology and radiotherapy. 2014;(3):37-42. (In Russ.)
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