Abstract
Fiber clustering algorithms are employed to find patterns in the structural connections of the human brain as traced by tractography algorithms. Current clustering algorithms often require the calculation of large similarity matrices and thus do not scale well for datasets beyond 100,000 streamlines. We extended and adapted the 2D vector field k–means algorithm of Ferreira et al. to find bundles in 3D tractography data from diffusion MRI (dMRI) data. The resulting algorithm is linear in the number of line segments in the fiber data and can cluster large datasets without the use of random sampling or complex multipass procedures. It copes with interrupted streamlines and allows multisubject comparisons.
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Dodero, L., Vascon, S., Murino, V., Bifone, A., Gozzi, A., Sona, D.: Automated multi-subject fiber clustering of mouse brain using dominant sets. Frontiers in Neuroinformatics 8, 87 (2015)
Ferreira, N., Klosowski, J.T., Scheidegger, C.E., Silva, C.T.: Vector field k–means: Clustering trajectories by fitting multiple vector fields. Computer Graphics Forum 32(3), 201–210 (2013)
Garyfallidis, E., Brett, M., Correia, M.M., Williams, G.B., Nimmo-Smith, I.: Quickbundles, a method for tractography simplification. Front. Neurosci. 6 (2012)
Guevara, P., Poupon, C.: Inference of a hardi fiber bundle atlas using a two-level clustering strategy. In: Jiang, T., Navab, N., Pluim, J.W., Viergever, M. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 550–557. Springer, Heidelberg (2010)
O’Donnell, L., Westin, C.F.: White matter tract clustering and correspondence in populations. In: Duncan, J., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 140–147. Springer, Heidelberg (2005)
O’Donnell, L.J., Golby, A.J., Westin, C.F.: Fiber clustering versus the parcellation-based connectome. NeuroImage 80, 283–289 (2013)
Visser, E., Nijhuis, E.H., Buitelaar, J.K., Zwiers, M.P.: Partition-based mass clustering of tractography streamlines. NeuroImage 54(1), 303–312 (2011)
Wang, X., Grimson, W.E.L., Westin, C.F.: Tractography segmentation using a hierarchical dirichlet processes mixture model. NeuroImage 54(1), 290–302 (2011)
Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., Deriche, R.: Unsupervised white matter fiber clustering and tract probability map generation: Applications of a gaussian process framework for white matter fibers. NeuroImage 51(1), 228–241 (2010)
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)
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Reichenbach, A., Goldau, M., Heine, C., Hlawitschka, M. (2015). V–Bundles: Clustering Fiber Trajectories from Diffusion MRI in Linear Time. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_24
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DOI: https://doi.org/10.1007/978-3-319-24553-9_24
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