Abstract
Diffusion-weighted imaging and tractography offer a unique approach to probe the microarchitecture of brain tissue noninvasively. Whole brain tractography, however, produces an unstructured set of fiber trajectories, whereas clinical applications often demand targeted tracking of specific bundles. This work presents a novel, hybrid approach to fiber bundle segmentation, using spectral embedding and supervised learning. Training data of 20 healthy subjects is labeled with a parcellation-based method, and used to train support vector machine and random forest classifiers. Cross-validation was used to avoid overfitting. Results on testing data of five independent subjects show a clear improvement over unsupervised methods. Moreover, estimating the label probabilities allows to reduce the effect of outliers.
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
References
Basser, P.J., Mattiello, J., LeBihan, D.: Mr diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)
Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using dt-mri data. Magn. Reson. Med. 44(4), 625–632 (2000)
Beaulieu, C.: The basis of anisotropic water diffusion in the nervous system–a technical review. NMR Biomed. 15(7–8), 435–455 (2002)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E.: Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. 96(18), 10422–10427 (1999)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Descoteaux, M., Deriche, R., Knosche, T., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the nystrom method. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 214–225 (2004)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., Van Essen, D.C., Jenkinson, M.: The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124 (2013)
Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.F.: Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage 61(4), 1083–1099 (2012)
Maddah, M., Grimson, W.E.L., Warfield, S.K., Wells, W.M.: A unified framework for clustering and quantitative analysis of white matter fiber tracts. Med. Image Anal. 12(2), 191–202 (2008)
Mori, S., van Zijl, P.: Fiber tracking: principles and strategies—a technical review. NMR Biomed. 15(7–8), 468–480 (2002)
O’Donnell, L.J., Westin, C.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26(11), 1562–1575 (2007)
O’Donnell, L.J., Golby, A.J., Westin, C.F.: Fiber clustering versus the parcellation-based connectome. NeuroImage 80, 283–289 (2013)
Parker, G.J.: Probabilistic fiber tracking. In: Jones, D.K. (ed.) Diffusion MRI: Theory, Methods, and Applications, pp. 396–408. Oxford University Press, Oxford (2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Tournier, J., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)
Tournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65(6), 1532–1556 (2011)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)
Wassermann, D., Makris, N., Rathi, Y., Shenton, M., Kikinis, R., Kubicki, M., Westin, C.F.: On describing human white matter anatomy: the white matter query language. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013. Lecture Notes in Computer Science, vol. 8149, pp. 647–654. Springer, Berlin (2013)
Acknowledgements
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Vercruysse, D., Christiaens, D., Maes, F., Sunaert, S., Suetens, P. (2014). Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning. In: O'Donnell, L., Nedjati-Gilani, G., Rathi, Y., Reisert, M., Schneider, T. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-11182-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-11182-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11181-0
Online ISBN: 978-3-319-11182-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)