Abstract
This paper presents a learning based method for automatic extraction of the major cortical sulci from MRI volumes or extracted surfaces. Instead of using a few pre-defined rules such as the mean curvature properties, to detect the major sulci, the algorithm learns a discriminative model by selecting and combining features from a large pool of candidates. We used the Probabilistic Boosting Tree algorithm [16] to learn the model, which implicitly discovers and combines rules based on manually annotated sulci traced by neuroanatomists. The algorithm almost has no parameters to tune and is fast because of the adoption of integral volume and 3D Haar filters. For a given approximately registered MRI volume, the algorithm computes the probability of how likely it is that each voxel lies on a major sulcus curve. Dynamic programming is then applied to extract the curve based on the probability map and a shape prior. Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect makes the approach flexible and it also works on extracted cortical surfaces.
Keywords
- Ground Truth
- Williams Syndrome
- Cortical Surface
- Automatic Extraction
- Discriminative Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Bartesaghi, A., Sapiro, G.: A System for the Generation of Curves on 3D Brain Images. Human Brain Map. 14, 1–15 (2001)
Besag, J.: Efficiency of pseudo-likelihood estimation for simple Gaussian fields. Biometrika 64, 616–618 (1977)
Caunce, A., Taylor, C.J.: Building 3D Sulcal Models Using Local geometry. Medical Image Analysis 5, 69–80 (2001)
Freund, Y., Schapire, R.: A Decision-Theoretic Generalization of On-line Learning and An Application to Boosting. J. of Computer and Sys. Sci. (1997)
Hellier, P., Barillot, C.: Coupling Dense and Landmark-Based Approaches for Nonrigid Registration. IEEE Trans. Med. Imaging 22(2) (February 2003)
Khaneja, N., Miller, M.I., Grenander, U.: Dynamic Programming Generation of Curves on Brain Surfaces. PAMI 20(11) (1998)
LeGoualher, G., Procyk, E., Collins, D.L., Venugopal, R., Barillot, C., Evans, A.C.: Automated extraction and variability analysis of sulcal neuroanatomy. IEEE Trans. Med. Imaging 18(3) (March 1999)
Lohmann, G.: Extracting Line Representations of Sulcal and Gyral Patterns in MR Images of the Hunam Brain. IEEE Trans. on Medical Imag. 17(6) (December 1998)
Ono, M., Kubik, S., Abernathey, S.D.: Atlas of the Cerebral Sulci. Thieme Medical, New York (1990)
Rettmann, M.E., Han, X., Xu, C., Prince, J.L.: Automated sulcal segmentation using watersheds on the cortical surface. Neuroimage 15(2), 329–344 (2002)
Riviere, D., Mangin, J.F., Papadopoulos-Orfanos, D., Martinez, J.M., Frouin, V., Regis, J.: Automatic Recognition of Cortical Sulci of the Human Brain Using A Congregation of Neural Networks. Medical Image Analysis, 77–92 (2002)
Thompson, P.M., et al.: Mapping Cortical Change in Alzheimer’s Disease, Brain Development, and Schizophrenia. NeuroImage 23(Suppl. 1), S2–S18 (2004)
Thompson, P.M., Lee, A.D., Dutton, R.A., Geaga, J.A., Hayashi, K.M., Bellugi, K.U., Galaburda, A.M., Korenberg, J.R., Mills, D.L., Toga, A.W., Reiss, A.L.: Abnormal Cortical Complexity and Thickness Profiles Mapped in Williams Syndrome. J. of Neuroscience 25(18) (April 2005)
Tao, X., Prince, J.L., Davatzikos, C.: Using a Statistical Shape Model to Extract Sulcal Curves on the Outer Cortex of the Human Brain. IEEE Tran. on Medical Imag. 21(5) (May 2002)
Thirion, J.P., Gourdon, A.: Computing the Differential Characteristics of Isointensity Surfaces. Computer Vision and Image Understanding 61(2) (1995)
Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: Proc. of ICCV (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zheng, S. et al. (2006). A Learning Based Algorithm for Automatic Extraction of the Cortical Sulci. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866565_85
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DOI: https://doi.org/10.1007/11866565_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44707-8
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