Abstract
In this paper, we study the recognition of about 60 sulcal structures over a new T1 MRI database of 62 subjects. It continues our previous work [7] and more specifically extends the localization model of sulci (SPAM). This model is sensitive to the chosen common space during the group study. Thus, we focus the current work on refining this space using registration techniques. Nevertheless, we also benefit from the sulcuswise localization variability knowledge to constrain the normalization. So, we propose a consistent Bayesian framework to jointly identify and register sulci, with two complementary normalization techniques and their detailed integration in the model: a global rigid transformation followed by a piecewise rigid-one, sulcus after sulcus. Thereby, we have improved the sulci labeling quality to a global recognition rate of 86%, and moreover obtained a basic but robust registration technique.
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References
Régis, J., Mangin, J.-F., Ochiai, T., Frouin, V., Rivière, D., Cachia, A., Tamura, M., Samson, Y.: Sulcal root generic model: a hypothesis to overcome the variability of the human cortex folding patterns. Neurol. Med. Chir. 45, 1–17 (2005)
Poupon, C., Poupon, F., Allirol, L., Mangin, J.-F.: A database dedicated to anatomo-functional study of human brain connectivity. In: 12th Ann. Meet. Org. Human Brain Mapping (2006)
Pinel, P., Thirion, B., Meriaux, S., Jobert, A., Serres, J., Bihan, D.L., Poline, J.-B., Dehaene, S.: Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neurosci. 8, 91 (2007)
Mangin, J.-F., Frouin, V., Bloch, I., Régis, J., Lopez-Krahe, J.: From 3D Magnetic Resonance Images to Structural Representations of the Cortex Topography Using Topology Preserving Deformations. Journal of Mathematical Imaging and Vision 5, 297–318 (1995)
Rivière, D., Mangin, J.-F., Papadopoulos-Orfanos, D., Martinez, J.-M., Frouin, V., Régis, J.: Automatic Recognition of Cortical Sulci Using a Congregation of Neural Networks. MIA 6, 77–92 (2002)
Perrot, M., Rivière, D., Mangin, J.-F.: Identifying cortical sulci from localization, shape and local organization. In: ISBI, pp. 420–423 (2008)
Cathier, P., Mangin, J.-F., Pennec, X., Rivière, D., Papadopoulos-Orfanos, D., Régis, J., Ayache, N.: Multisubject non-rigid registration of brain MRI using intensity and geometric features. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 734–742. Springer, Heidelberg (2001)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)
Powell, M.J.D.: An Efficient Method for Finding the Minimum of a Function of Several Variables Without Calculating Derivatives. Computer Journal 7, 155–162 (1964)
Mardia, K.V., Jupp, P.: Directional Statistics, 2nd edn. John Wiley and Sons Ltd., Chichester (2000)
Talairach, J., Tournoux, P.: Co-planar Stereotaxic Atlas of the Human Brain. Georg Thieme Verlag, Stuttgart (1988)
Evans, A.C., Collins, D.L., Neelin, P., MacDonald, D., Kamber, M., Marrett, T.S.: Three-Dimensional Correlative Imaging: Applications in Human Brain Mapping. Functional Neuroimaging 14, 145–161 (1994)
Le Goualher, G., Collins, D.L., Barillot, C., Evans, A.C.: Automatic Identification of Cortical Sulci Using a 3D Probabilistic Atlas. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 509–518. Springer, Heidelberg (1998)
Lohmann, G., von Cramon, D.Y.: Automatic labelling of the human cortical surface using sulcal basins. Medical image analysis 4(3), 179–188 (2000)
Lohmann, G., von Cramon, D.Y., Colchester, A.C.F.: Deep Sulcal Landmarks Provide an Organizing Framework for Human Cortical Folding. Cerebral Cortex 18(6), 1415–1420 (2008)
Tosun, D., Prince, J.L.: A Geometry-Driven Optical Flow Warping for Spatial Normalization of Cortical Surfaces. TMI 27(12), 1739–1753 (2008)
Yang, F., Kruggel, F.: Optimization Algorithms for Labeling Brain Sulci Based on Graph Matching. In: ICCV, pp. 1–7 (2007)
Vaillant, M., Davatzikos, C.: Hierarchical Matching of Cortical Features for Deformable Brain Image Registration. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 182–195. Springer, Heidelberg (1999)
Yeo, B., Sabuncu, M., Desikan, R., Fischl, B., Golland, P.: Effects of registration regularization and atlas sharpness on segmentation accuracy. Med. Image Anal. 12(5), 603–615 (2008)
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Perrot, M., Rivière, D., Tucholka, A., Mangin, JF. (2009). Joint Bayesian Cortical Sulci Recognition and Spatial Normalization. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_15
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DOI: https://doi.org/10.1007/978-3-642-02498-6_15
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