Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering

Abstract : The clustering of fi bers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fi bers decoding their connectivity to gray matter regions. We then simplify the clustering task by rst deriving a compact encoding of our representation via the logit transformation. Furthermore, we defi ne a distance between fi bers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting ber bundles without needing to register the corresponding scans to a common coordinate system. We con firm these qualitative findings via a simple statistical analyse of the fi ber bundles.
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Gee, J. C. and Joshi, Sarang and Pohl, Kilian M. and Wells, William M. and Zollei, L. IPMI 2013 - Information Processing in Medical Imaging, Jun 2013, Asilomar, United States. Springer, 7917, pp.730-741, 2013, LNCS. 〈10.1007/978-3-642-38868-2_61〉
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Birkan Tunç, Alex Smith,, Demian Wasserman, Xavier Pennec, William M. Wells, et al.. Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering. Gee, J. C. and Joshi, Sarang and Pohl, Kilian M. and Wells, William M. and Zollei, L. IPMI 2013 - Information Processing in Medical Imaging, Jun 2013, Asilomar, United States. Springer, 7917, pp.730-741, 2013, LNCS. 〈10.1007/978-3-642-38868-2_61〉. 〈hal-00846431〉

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