Automatic identificaiton of cortical sulci using a 3D probabilistic atlas

  • Georges Le Goualher
  • D. Louis Collins
  • Christian Barillot
  • Alan C. Evans
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


We present an approach which performs the automatic labeling of the main cortical sulci using a priori information for the 3D spatial distribution of these entities. We have developed a methodology to extract the 3D cortical topography of a particular subject from in vivo observations obtained through MRI. The cortical topography is encoded in a relational graph structure composed of two main features: arcs and vertices. Each vertex contains a parametric surface representing the buried part of a sulcus. Points on this parametric surface are expressed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). Arcs represent the connections between these entities. Manual sulcal labeling is performed by tagging a sulcal surface in the 3-D graph and selecting from a menu of candidate sulcus names. Automatic labeling is dependent on a probabilistic atlas of sulcal anatomy derived from a set of 51 graphs that were labeled by an anatomist. We show how these 3D sulcal spatial distribution maps can be used to perform the identification of the cortical sulci. We focus our attention on the peri-central area (including pre-central, post-central and central sulci). Results show that the use of spatial priors permit automatic identification of the main sulci with a good accuracy.


active model probabilistic atlas cerebral cortex sulci MRI 


  1. 1.
    J.C. Mazziota, A.W. Toga, A.C. Evans, P. Fox, and J. Lancaster. A probar bilistic atlas of the human brain: theory and rationale for its development. Neuroimage, 2:89–101, 1995.CrossRefGoogle Scholar
  2. 2.
    S. Sandor and R. Leahy. Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Transactions on Medical Imaging, 16(1):41–54, Feb 1997.CrossRefPubMedGoogle Scholar
  3. 3.
    N. Royackkers, H. Fawal, M. Desvignes, M. Revenu, and J.M. Travere. Feature extraction for cortical sulci identification. In 9th Scandinavian conference on image analysis, volume 2, pages 110–121, June 1995.Google Scholar
  4. 4.
    G. Subsol, J.P. Thirion, and N. Ayache. Application of an automatically build 3D morphometric atlas: Study of cerebral ventricle shape. In Karl Heinz Hohne and Ron Kikinis, editors, Visualization in Biomedical Computing (VBC), pages 373–382. LNCS 1131, Sept 1996.Google Scholar
  5. 5.
    G. Le Goualher, C. Barillot, Y. Bizais, and J-M. Scarabin. 3D segmentation of cortical sulci using active models. In SPIE Proceedings of Medical Imaging: Image Processing, volume 2710, pages 254–263, 1996.CrossRefGoogle Scholar
  6. 6.
    Marc Vaillant, Christos Davatzikos, and R.Nick Bryan. Finding 3D parametric representations of deep cortical folds. In Workshops on Math. Methods in Biomedical Image Analysis, pages 151–159, 1996.Google Scholar
  7. 7.
    G. Szekely, Ch. Brechuhler, O. Kubler, R. Ogniewickz, and T. Budinger. Mapping the human cerebral cortex using 3-D medial manifolds. In SPIE Visualization in Biomedical Computing, volume 1808, pages 130–144, 1992.CrossRefGoogle Scholar
  8. 8.
    J.F. Mangin, J. Regis, I. Bloch, V. Frouin, Y. Samson, and J. Lopez-Krahe. A MRF based random graph modelling the human cortical topography. In CVRMed, pages 177–183, Nice, France, April 1995. LNCS 905.Google Scholar
  9. 9.
    Gabriele Lohmann, Frithjof Kruggel, and D.Yves von Cramon. Automatic detection of sulcal bottom lines in MR images of the human brain. In Information Processing in Medical Imaging (IPMI), pages 369–374. LNCS 1230, 1997.Google Scholar
  10. 10.
    R. Bajcsy and S. Kovacic. Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 46:1–21, 1989.CrossRefGoogle Scholar
  11. 11.
    D.L. Collins, G. Le Goualher, R. Venugopal, Z. Caramanos, A.C. Evans, and C. Barillot. Cortical constraints for non-linear cortical registration. In K.H. Höene, editor, Visualization in Biomedical Computing, pages 307–316, Hamburg, Sept 1996.Google Scholar
  12. 12.
    H. Fawall. Contribution à l’étude d’une base de connaissances adaptée à la définition des sillons du cortex cérébral humain. PhD thesis, Université de Caen, 1995.Google Scholar
  13. 13.
    M. Desvignes, N. Royackkers, H. Fawal, and M. Revenu. Detection and identification of sulci on 3D MRI. In Human Brain Mapping, volume 4, page S410, 1997.Google Scholar
  14. 14.
    F. Kruggel. Automatical adaptation of anatomical masks to the neocortex. In Proceedings of the 1th International Conference CVRMed, volume LNCS 905, France, April 1995.Google Scholar
  15. 15.
    A. Zijdenbos, A.C. Evans, F. Riahi, J. Sled, J. Chui, and V. Kollokian. Automatic quantification of multiple sclerosis lesion volume using stereotaxic space. In Visualisation in Biomedical Computing (VBC), pages 439–448. 4th International Conference, LNCS 1131, September 1996.Google Scholar
  16. 16.
    John G. Sled, Alex P. Zijdenbos, and Alan C. Evans. A non-parametric method for automatic correction of intensity non-uniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1):87–97, February 1998. submitted December 1996.CrossRefPubMedGoogle Scholar
  17. 17.
    D.L. Collins, P. Neelin, T.M. Peters, and A.C. Evans. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2):192–205, March, April 1994.CrossRefPubMedGoogle Scholar
  18. 18.
    D. MacDonald, D. Avis, and A.C. Evans. Multiple surface identification and matching in Magnetic Resonance Images. In Visualization in Biomedical Computing (VBC), volume 2359, pages 160–169, Rochester, 1994.Google Scholar
  19. 19.
    J. Serra. Image analysis and mathematical morphology. Academic Press, London, 1982.Google Scholar
  20. 20.
    G. Le Goualher, C. Barillot, L. Le Briquer, and Y. Bizais. 3D detection and representation of cortical sulci. In Computer Assisted Radiology, CAR’95, 1995.Google Scholar
  21. 21.
    G. Malandin, G. Bertrand, and N. Ayache. Topological segmentation of discrete surfaces. In IEEE Conference on Computer Vision and Pattern Recognition, pages 444–449, 1991.Google Scholar
  22. 22.
    G. Le Goualher, C. Barillot, and Y. Bizais. Modeling cortical sulci using active ribbons. International Journal of Pattern Recognition and Artificial Intelligence, 11(8):1295–1315, 1997.CrossRefGoogle Scholar
  23. 23.
    J. Talairach and P. Tournoux. Co-planar stereotactic atlas of the human brain: 3-Dimensional proportional system: an approach to cerebral imaging. Georg Thieme Verlag, Stuttgart, New York, 1988.Google Scholar
  24. 24.
    A.C Evans, D.L. Collins, C. Holmes, T. Paus, D. MacDonald, A. Zijdenbos, A. Toga, P. Fox, J. Lancaster, and J. Mazziota. A 3D probabilistic atlas of normal human neuroanatomy. In Human Brain Mapping, volume 4, page S349, 1997.Google Scholar
  25. 25.
    A. Caunce and C.J. Taylor. 3D point distribution models of cortical sulci. In ICCV, Bombay, India, january 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Georges Le Goualher
    • 1
    • 2
  • D. Louis Collins
    • 1
  • Christian Barillot
    • 2
  • Alan C. Evans
    • 1
  1. 1.McConnell Brain Imaging Center, Montréal Neurological InstituteMcGill UniversityMontréalCanada
  2. 2.Laboratoire Signaux et Images en MédecineUniversité de RennesFrance

Personalised recommendations