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Brain Lesions, Introduction

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9556))

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Abstract

A brain lesion is a brain tissue abnormality which can be seen on a neurological scan, such as magnetic resonance imaging or computerized tomography. Brain tumor, multiple sclerosis, stroke and traumatic brain injuries are different diseases and accidents affecting in different ways the brain. Their unpredictable appearance and shape make them challenging to be segmented in multi-modal brain imaging. Nevertheless, they share similarities in the way they appear in medical images.

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References

  1. Jankovic, J.: Parkinsons disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79(4), 368–376 (2008)

    Article  Google Scholar 

  2. Dubois, B., Feldman, H.H., Jacova, C., DeKosky, S.T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., et al.: Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 6(8), 734–746 (2007)

    Article  Google Scholar 

  3. Steinman, L.: Multiple sclerosis: a two-stage disease. Nat. Immunol. 2(9), 762–764 (2001)

    Article  Google Scholar 

  4. Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., Galanis, E., DeGroot, J., Wick, W., Gilbert, M.R., Lassman, A.B., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28(11), 1963–1972 (2010)

    Article  Google Scholar 

  5. van den Bent, M., Wefel, J.S., Schiff, D., Taphoorn, M., Jaeckle, K., Junck, L., Armstrong, T., Choucair, A., Waldman, A., Gorlia, T., et al.: Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol. 12(6), 583–593 (2011)

    Article  Google Scholar 

  6. Leray, E., Yaouanq, J., Le Page, E., Coustans, M., Laplaud, D., Oger, J., Edan, G.: Evidence for a two-stage disability progression in multiple sclerosis. Brain 133(7), 1900–1913 (2010)

    Article  Google Scholar 

  7. Filippi, M., Rocca, M.A., Arnold, D.L., Bakshi, R., Barkhof, F., De Stefano, N., Fazekas, F., Frohman, E., Wolinsky, J.S.: EFNS guidelines on the use of neuroimaging in the management of multiple sclerosis. Eur. J. Neurol. 13(4), 313–325 (2006)

    Article  Google Scholar 

  8. Pexman, J.W., Barber, P.A., Hill, M.D., Sevick, R.J., Demchuk, A.M., Hudon, M.E., Hu, W.Y., Buchan, A.M.: Use of the Alberta stroke program early CT score (aspects) for assessing CT scans in patients with acute stroke. Am. J. Neuroradiol. 22(8), 1534–1542 (2001)

    Google Scholar 

  9. Kinnunen, K.M., Greenwood, R., Powell, J.H., Leech, R., Hawkins, P.C., Bonnelle, V., Patel, M.C., Counsell, S.J., Sharp, D.J.: White matter damage and cognitive impairment after traumatic brain injury. Brain 134(pt. 2), 449–463 (2010)

    Google Scholar 

  10. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., et al.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4), 1487–1505 (2006)

    Article  Google Scholar 

  11. Black, P.M., Moriarty, T., Alexander III, E., Stieg, P., Woodard, E.J., Gleason, P.L., Martin, C.H., Kikinis, R., Schwartz, R.B., Jolesz, F.A.: Development and implementation of intraoperative magnetic resonance imaging and its neurosurgical applications. Neurosurgery 41(4), 831–845 (1997)

    Article  Google Scholar 

  12. Brandsma, D., Stalpers, L., Taal, W., Sminia, P., van den Bent, M.J.: Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 9(5), 453–461 (2008)

    Article  Google Scholar 

  13. Kelly, P.J., Daumas-Duport, C., Kispert, D.B., Kall, B.A., Scheithauer, B.W., Illig, J.J.: Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J. Neurosurg. 66(6), 865–874 (1987)

    Article  Google Scholar 

  14. Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G.: Automatic brain tumor segmentation by subject specific modification of atlas priors 1. Acad. Radiol. 10(12), 1341–1348 (2003)

    Article  Google Scholar 

  15. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)

    Article  Google Scholar 

  16. Lee, C.-H., Wang, S., Murtha, A., Brown, M.R.G., Greiner, R.: Segmenting brain tumors using pseudo–conditional random fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 359–366. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)

    Article  Google Scholar 

  18. Karimaghaloo, Z., Rivaz, H., Arnold, D.L., Collins, D.L., Arbel, T.: Adaptive voxel, texture and temporal conditional random fields for detection of gad-enhancing multiple sclerosis lesions in brain MRI. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 543–550. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  21. Gerig, G., Jomier, M., Chakos, M.: Valmet: a new validation tool for assessing and improving 3D object segmentation. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 516–523. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  22. Gevaert, O., Mitchell, L.A., Achrol, A.S., Xu, J., Echegaray, S., Steinberg, G.K., Cheshier, S.H., Napel, S., Zaharchuk, G., Plevritis, S.K.: Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273(1), 168–174 (2014)

    Article  Google Scholar 

  23. Reuter, M., Wolter, F.E., Shenton, M., Niethammer, M.: Laplace-Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis. Comput. Aided Des. 41(10), 739–755 (2009)

    Article  Google Scholar 

  24. Gerardin, E., Chételat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.S., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al.: Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4), 1476–1486 (2009)

    Article  Google Scholar 

  25. Crimi, A., Commowick, O., Maarouf, A., Ferré, J.C., Bannier, E., Tourbah, A., Berry, I., Ranjeva, J.P., Edan, G., Barillot, C.: Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. PLoS ONE 9(4), e93024 (2014)

    Article  Google Scholar 

  26. Babalola, K.O., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T., Jenkinson, M., Rueckert, D.: An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage 47(4), 1435–1447 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Istituto Italiano di Tecnologia (IIT), Via Morego, 30, 16163, Genova, Italy

    Alessandro Crimi

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  1. Alessandro Crimi
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Correspondence to Alessandro Crimi .

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Editors and Affiliations

  1. Istituto Italiano di Tecnologia (IIT), Genova, Italy

    Alessandro Crimi

  2. TU München, Computer Science, München, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Crimi, A. (2016). Brain Lesions, Introduction. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-30858-6_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30857-9

  • Online ISBN: 978-3-319-30858-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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