Semiautomatic Segmentation of the Medial Temporal Lobe Anatomical Structures

  • M. Rincón
  • E. Díaz-López
  • F. Alfaro
  • A. Díez-Peña
  • T. García-Saiz
  • M. Bachiller
  • A. Insausti
  • R. Insausti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Medial temporal lobe (MTL) is a region of the brain related with processing and declarative memory consolidation. Structural changes in this region are directly related with Alzheimer’s disease and other dementias. Manual delimitation of these structures is very time consuming and error prone. Automatic methods are needed in order to solve these problems and make it available in the clinical practice. Unfortunately, automatic methods are not robust enough yet. The use of semiautomatic methods provides an intermediate solution with the advantages of automatic methods under the supervision of the expert. This paper propose two semiautomatic methods oriented to make the delineation of the MTL structures easy, robust and fast.

Keywords

semiautomatic segmentation medial temporal lobe 

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References

  1. 1.
    Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis I: Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999)CrossRefGoogle Scholar
  2. 2.
    Fischl, B., Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M.: Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex 14, 11–22 (2004)CrossRefGoogle Scholar
  3. 3.
    Ibáñez, L., Schroeder, W., Ng, L., Cates, J., Consortium, T.I.S., Hamming, R.: The ITK Software Guide. Kitware, Inc. (January 2003)Google Scholar
  4. 4.
    Insausti, R., Juottonen, K., Soininen, H., Insausti, A., Partanen, K., Vainio, P., Laakso, M.P., Pitkanen, A. M.: VolumetricAnalysis of the Human Entorhinal, Perirhinal, and Temporopolar Cortices. AJNR Am. J. Neuroradiol. 19, 656–671 (1998)Google Scholar
  5. 5.
    Insausti, R., Insausti, A.M., Mansilla, F., Abizanda, P., Artacho-Pérula, E., Arroyo-Jimenez, M.M., Martinez-Marcos, A., Marcos, P.: The human parahippocampal gyrus. In: 3th Annual Meeting of Society for Neuroscience Anatomical and MRI correlates, New Orleans, USA (November 2003)Google Scholar
  6. 6.
    Insausti, R., Rincón, M., González-Moreno, C., Artacho-Pérula, E., Díez-Peña, A., García-Saiz, T.: Neurobiological significance of automatic segmentation: Application to the early diagnosis of alzheimer’s disease. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 134–141. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Juottonen, K., Laakso, M.P., Insausti, R., Lehtovirta, M., Pitkanen, A., Partanen, K., Soininen, H.: Volumes of the Entorhinal and Perirhinal Cortices in Alzheimer’sDisease. Neurobiology of Aging 19 (1998)Google Scholar
  8. 8.
    Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., Lundervold, A.: Evaluation of Automated Brain MR Image Segmentation and Volumetry Methods. Human Brain Mapping 30, 1310–1327 (2009)CrossRefGoogle Scholar
  9. 9.
    Hu, Y.J., Grossberg, M.D., Mageras, G.S.: Semiautomatic medical image segmentation with adaptive local statistics in conditional random fields framework. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3099–3102 (2008)Google Scholar
  10. 10.
    Nowinski, et al.: A New Presentation and Exploration of Human Cerebral Vasculature Correlated With Surface and Sectional. Neuroanatomy Anat. Sci. Ed. 2, 24–33 (2009)CrossRefGoogle Scholar
  11. 11.
    Pichon, E., Tannenbaum, A., Kikinis, R.: A statistically based flow for image segmentation. Med. Image Anal. 8(3), 267–274 (2004)CrossRefGoogle Scholar
  12. 12.
    Pieper, S., Lorensen, B., Schroeder, W., Kikinis, R.: The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community. In: Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006, vol. 1, pp. 698–701 (2006)Google Scholar
  13. 13.
    Sánchez-Benavidesab, G., Gómez-Ansónc, B., Sainzd, A., Vivesd, Y., Delfinod, M., Peña-Casanova, J.: Manual validation of FreeSurfer’s automated hippocampal segmentation in normal aging, mild cognitive impairment. Alzheimer Disease subjects 181(3), 219–225 (2010)Google Scholar
  14. 14.
    Scoville, W.B., Milner, B.: Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21 (1957)CrossRefGoogle Scholar
  15. 15.
    Squire, L.R., Stark, C., Clark, R.E.: The medial temporal lobe. Annu. Rev. Neurosci. 27, 279–306 (2004)CrossRefGoogle Scholar
  16. 16.
    Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E.J., Johansen- Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, 208–219 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Rincón
    • 1
  • E. Díaz-López
    • 1
  • F. Alfaro
    • 1
  • A. Díez-Peña
    • 2
  • T. García-Saiz
    • 1
  • M. Bachiller
    • 1
  • A. Insausti
    • 4
  • R. Insausti
    • 3
  1. 1.Dept. Inteligencia Artificial. E.T.S.I. InformáticaUniversidad Nacional de Educación a DistanciaMadridSpain
  2. 2.DEIMOS Space S.L.U., Ronda de PonienteMadridSpain
  3. 3.Human Neuroanatomy Laboratory, School of MedicineUniversity of Castilla-La ManchaAlbaceteSpain
  4. 4.Departamento de Ciencias de la SaludUniversidad Pública de NavarraPamplonaSpain

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