Advertisement

Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention-Deficit/Hyperactivity Disorder

  • Laura Igual
  • Joan Carles Soliva
  • Roger Gimeno
  • Sergio Escalera
  • Oscar Vilarroya
  • Petia Radeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.

Keywords

Automatic Caudate Segmentation Attention-Deficit/Hyperactivity Disorder ADHD Diagnosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Carmona, S., Vilarroya, O., et al.: Global and regional gray matter reductions in ADHD: A voxel-based morphometric study. Neuroscience Letters 389(2), 88–93 (2005)CrossRefGoogle Scholar
  2. 2.
    Castellani, U., Mirtuono, P., Murino, V., Bellani, M., Rambaldelli, G., Tansella, M., Brambilla, P.: A New Shape Diffusion Descriptor for Brain Classification. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 426–433. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Castellanos, F.X., Lee, P.P., Sharp, W., Jeffries, N.O., Greenstein, D.K., Clasen, L.S., et al.: Developmental Trajectories of Brain Volume Abnormalities in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder. The Journal of the American Medical Association 288(14), 1740–1748 (2002)CrossRefGoogle Scholar
  4. 4.
    Gerig, G., Styner, M., Jones, D., Weinberger, D., Lieberman, J.: Shape Analysis of Brain Ventricles Using SPHARM. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 171–178 (August 2001), http://dx.doi.org/10.1109/MMBIA.2001.991731
  5. 5.
    Igual, L., Soliva, J.C., Hernandez-Vela, A., Escalera, S., Jimenez, X., Vilarroya, O., Radeva, P.: A fully-automatic caudate nucleus segmentation of brain MRI: Application in pediatric attention- deficit/hyperactivity disorder volumetric analysis. BioMedical Engineering Online 10(105)Google Scholar
  6. 6.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts. PAMI 26, 65–81 (2004)CrossRefGoogle Scholar
  7. 7.
    Seidman, L.J., Valera, E.M., Makris, N.: Structural brain imaging of attention-deficit/hyperactivity disorder. Biological Psychiatry 57(11), 1263–1272 (2005)CrossRefGoogle Scholar
  8. 8.
    Soliva, J., Fauquet, J., Bielsa, A., Rovira, M., Carmona, S., Ramos-Quiroga, J., Hilferty, J., Bulbena, A., Casas, M., Vilarroya, O.: Quantitative MR analysis of the caudate abnormalitites in pediatric adhd: Proposal for a diagnostic test. Psychiatric Research: Neuroimaging (2010)Google Scholar
  9. 9.
    Talairach, J., Tournoux, P.: Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System - an Approach to Cerebral Imaging. Thieme Medical Publishers (1988)Google Scholar
  10. 10.
    Trèmols, V., Bielsa, A., Soliva, J.C., Raheb, C., Carmona, S., Tomàs, J., Gispert, J., Rovira, M., Fauquet, J., Tobeña, A., Bulbena, A., Vilarroya, O.: Differential abnormalities of the head and body of the caudate nucleus in attention deficit-hyperactivity disorder. Psychiatric Research 163(3), 270–278 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhou, H., Schaefer, G., Shi, C.: A mean shift based fuzzy c-means algorithm for image segmentation. In: Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society 2008, pp. 3091–3094 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Laura Igual
    • 1
    • 2
  • Joan Carles Soliva
    • 3
    • 4
  • Roger Gimeno
    • 2
  • Sergio Escalera
    • 1
    • 2
  • Oscar Vilarroya
    • 3
    • 4
  • Petia Radeva
    • 1
    • 2
  1. 1.Department of Applied Mathematics and AnalysisUniversitat of BarcelonaSpain
  2. 2.Computer Vision Center of BarcelonaSpain
  3. 3.Unitat de Recerca en Neurociència Cognitiva, Department of PsychiatryUniversitat Autònoma de BarcelonaSpain
  4. 4.Fundació IMIMSpain

Personalised recommendations