Medinria fiber validation3/15/2023 ![]() Tsai, A., Westin, C.F., Hero, A.O., Willsky, A.S.: Fiber tract clustering on manifolds with dual rooted-graphs. ![]() IEEE TMI 26(11), 1562–1575 (2007)īrun, A., Park, H.J., Knutsson, H., Westin, C.F.: Coloring of DT-MRI fiber traces using laplacian eigenmaps. ODonnell, L., Westin, C.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. In: Barillot, C., Haynor, D.R., Hellier, P. Magnetic Resonance in Medicine 48, 97–104 (2002)īrun, A., Knutsson, H., Park, H.J., Shenton, M.E., Westin, C.F.: Clustering fiber traces using normalized cuts. European Journal of Applied Physiology 93(3), 253–262 (2004)ĭamon, B., Ding, Z., Anderson, A., Freyer, A., Gore, J.: Validation of diffusion tensor MRI-based muscle fiber tracking. Galban, C.J., Maderwald, S., Uffmann, K., de Greiff, A., Ladd, M.E.: Diffusive sensitivity to muscle architecture: a magnetic resonance diffusion tensor imaging study of the human calf. Gilbert, R.J., Napadow, V.J.: Three-dimensional muscular architecture of the human tongue determined in vivo with diffusion tensor magnetic resonance imaging. Journal of Magnetic Resonance Imaging 13, 534–546 (2001) This process is experimental and the keywords may be updated as the learning algorithm improves.īihan, D.L., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabrait, H.: Diffusion tensor imaging: Concepts and applications. These keywords were added by machine and not by the authors. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. These metrics are used to approximate the geodesic distances over the fiber manifold. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. In this paper, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle.
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