Automatic detection and labelling of the human cortical folds in magnetic resonance data sets
The folding of the cortical surface of the human brain varies dramatically from person to person. However, the folding pattern is not arbitrary. The cortical folds (also called “sulci”) often serve as landmarks for referencing brain locations, and the most pronounced sulci have names that are well established in the neuroanatomical literature. In this paper, we will present a method that both automatically detects and attributes neuroanatomical names to these folds using image analysis methods applied to magnetic resonance data of human brains. More precisely, we subdivide each fold into a number of substructures which we call sulcal basins, and attach labels to these basins. These sulcal basins form a complete parcellation of the cortical surface.
The algorithm reported here is important in the context of human brain mapping. Human brain mapping aims at establishing correspondences between brain function and brain anatomy. One of the most intriguing problems in this field is the high inter-personal variability of human neuroanatomy which makes studies across many subjects very difficult. Most previous attempts at solving this problem are based on various methods of image registration where MR data sets of different subjects are warped until they overlap. We believe that in the process of warping too much of the individual anatomy is destroyed so that relevant information is lost. The approach presented in this paper allows inter-personal comparisons without having to resort to image warping. Our concept of sulcal basins allows to establish a complete parcellation of the cortical surface into separate regions. These regions are neuroanatomically meaningful and can be identified from MR data sets across many subjects. At the same time, the parcellation is detailed enough to be useful for brain mapping purposes.
Unable to display preview. Download preview PDF.
- 1.M. Ono, S. Kubik, C.D. Abernathy. Atlas of the cerebral sulci. Georg Thieme Verlag, Stuttgart, New York, 1990.Google Scholar
- 8.J. Declerck, G. Subsol, J.-P. Thirion, N. Ayache. Automatic retrieval of anatomical structures in 3D medical images. In N. Ayache, editor, Computer Vision, Virtual Reality and Robotics in Medicine, pages 153–162, Nice, France, April 1995. Springer Lecture Notes, 905.Google Scholar
- 10.J.F. Mangin, V. Frouin, J. Regis, I. Bloch, P. Belin, Y. Samson. Towards better management of cortical anatomy in multi-modal multi-individual brain studies. Physica medica, XII(Supplement 1):103–107, June 1996.Google Scholar
- 11.A. Manceaux-Demiau, J.F. Mangin, J. Regis, Olivier Pizzato, V. Frouin. Differential features of cortical folds. In CVRMed, Grenoble, France, 1997.Google Scholar
- 12.M. Valliant, C. Davatzikos, R.N. Bryan. Finding 3D parametric representations of the deep cortical folds. In Proc. Mathematical Methods in biomedical image analysis (MMBIA 96), pages 151–157, San Francisco, CA, June 1996. IEEE Computer Society.Google Scholar
- 13.M. Valliant, C. Davatzikos. Mapping the cerebral sulci: application to morphological analysis of the cortex and to non-rigid registration. In Int. Conf. on Information Processing in Medical Imaging (IPMI 97), pages 141–154, Poultney, Vermont, USA, June 9–13 1997.Google Scholar
- 14.M. NÄf, O. Kübler, R. Kikinis, M.E. Shenton, G. Szekely. Characterization and recognition of 3D organ shape in medical image analysis using skeletonization. In Proc. Mathematical Methods in biomedical image analysis (MMBIA 96), pages 139–150, San Francisco, CA, June 1996. IEEE Computer Society.Google Scholar
- 15.G. Lohmann. A new approach to white matter segmentation in MR images. Technical report, Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany, 1997.Google Scholar
- 17.G. Borgefors. Distance transforms in arbitrary dimensions. Computer Vision, Graphics, and Image Processing, 27:321–345, 1984.Google Scholar
- 18.P.T. Fox, J.S. Perlmutter, M.E. Raichle. A stereotactic method of anatomical localization for positron emission tomography. Journal of Computer Assisted Tomography, 9(1): 141–153, Jan./Feb. 1985.Google Scholar
- 19.G.Lohmann. Extracting line representations of sulcal and gyral patterns in MR images of the human brain. Technical report, Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany, July 1997.Google Scholar
- 20.D. Ballard, C.M. Brown. Computer Vision. Prentice Hall, Englewood Cliffs, NJ, 1982.Google Scholar
- 21.E.A. Akkoyunlu. The enumeration of maximal cliques of large graphs. SIAM J. Comput., 2(1), March 1973.Google Scholar
- 22.F. Kruggel, G.Lohmann. Automatical adaptation of the stereotactical coordinate system in brain MRI data sets. In J. Duncan, editor, Int. Conf. on Information Processing in Medical Imaging (IPMI 97), Poultney, Vermont, USA, June 9–13 1997.Google Scholar