Abstract
In this work we study the problem of supervised tract segmentation from tractography data, a vectorial representation of the brain connectivity extracted from diffusion magnetic resonance images. We report a case study based on a dataset where for each tractography of three subjects the segmentation of eight major anatomical tracts was manually operated by expert neuroanatomists. Domain specific distances that encodes the dissimilarity of tracts do not allow to define a positive semi-definite kernel function. We show that a dissimilarity representation based on such distances enables the successful design of a classifier. This approach provides a robust encoding which proves to be effective using a linear classifier. Our empirical analysis shows that we obtain better tract segmentation than previously proposed methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashburner, J., Friston, K.J.: Voxel-Based Morphometry The Methods. NeuroImage 11(6), 805–821 (2000)
Balcan, M.-F., Blum, A., Srebro, N.: A theory of learning with similarity functions. Machine Learning 72(1), 89–112 (2008)
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophysical Journal 66(1), 259–267 (1994)
Bishop, C.M.: Pattern Recognition and Machine Learning, 1st edn. Information Science and Statistics. Springer, Heidelberg (2006); corr. 2nd printing edn. (October 2007)
Boser, B.E., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. In: Computational Learing Theory, pp. 144–152 (1992)
Bozzali, M., Falini, A., Franceschi, M., Cercignani, M., Zuffi, M., Scotti, G., Comi, G., Filippi, M.: White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. Journal of Neurology, Neurosurgery & Psychiatry 72(6), 742–746 (2002)
Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity-based Classification: Concepts and Algorithms. J. Mach. Learn. Res. 10, 747–776 (2009)
Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, corrected edn. Springer, Heidelberg (1996)
Dubuisson, M.P., Jain, A.K.: A modified Hausdorff distance for object matching. In: Proceedings of 12th International Conference on Pattern Recognition, vol. 1, pp. 566–568. IEEE Comput. Soc. Press, Los Alamitos (1994)
Evans, A.C., Collins, D.L., Millst, S.R., Brown, E.D., Kelly, R.L., Peters, T.M.: 3D statistical neuroanatomical models from 305 MRI volumes. In: Nuclear Science Symposium and Medical Imaging Conference, pp. 1813–1817 (1993)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Series in Statistics, corr. 3rd printing 5th printing. edn. Springer, Heidelberg (2009)
Maddah, M., Mewes, A.U.J., Haker, S., Grimson, W.E.L., Warfield, S.K.: Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 188–195. Springer, Heidelberg (2005)
Mori, S., van Zijl, P.C.M.: Fiber tracking: principles and strategies a technical review. NMR Biomed. 15(7-8), 468–480 (2002)
Nezamzadeh, M., Van Wedeen, J., Wang, R., Zhang, Y., Zhan, W., Young, K., Meyerhoff, D.J., Weiner, M.W., Schuff, N.: In-vivo investigation of the human cingulum bundle using the optimization of MR diffusion spectrum imaging. European Journal of Radiology 75(1) (July 2010)
O’Donnell, L.J., Westin, C.-F.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Transactions on Medical Imaging 26(11), 1562–1575 (2007)
Olivetti, E., Mognon, A., Greiner, S., Avesani, P.: Brain Decoding: Biases in Error Estimation. In: First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD) (August 2010)
Olivetti, E., Veeramachaneni, S., Greiner, S., Avesani, P.: Brain connectivity analysis by reduction to pair classification. In: 2nd International Workshop on Cognitive Information Processing (CIP), pp. 275–280 (June 2010)
Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Pekalska, E., Duin, R., Paclik, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189–208 (2006)
Pekalska, E., Paclik, P., Duin, R.P.W.: A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res. 2, 175–211 (2002)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization, pp. 185–208. MIT Press, Cambridge (1999)
Schiavo, R.A., Hand, D.J.: Ten More Years of Error Rate Research. International Statistical Review 68(3), 295–310 (2000)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 1st edn. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2001)
Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Van Wedeen, J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48(4), 577–582 (2002)
Wang, X., Grimson, Westin, C.-F.: Tractography segmentation using a hierarchical Dirichlet processes mixture model. NeuroImage 54(1), 290–302 (2011)
Zhang, S., Correia, S., Laidlaw, D.H.: Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber-Clustering Method. IEEE Transactions on Visualization and Computer Graphics 14(5), 1044–1053 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olivetti, E., Avesani, P. (2011). Supervised Segmentation of Fiber Tracts. In: Pelillo, M., Hancock, E.R. (eds) Similarity-Based Pattern Recognition. SIMBAD 2011. Lecture Notes in Computer Science, vol 7005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24471-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-24471-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24470-4
Online ISBN: 978-3-642-24471-1
eBook Packages: Computer ScienceComputer Science (R0)