Automatic White Matter Fiber Clustering Using Dominant Sets

We present an unsupervised approach based on the Dominant Sets framework to automatically segment the white matter fibers into bundles. This framework, rooted in the Game Theory, allows for the automatic determination of the number of clusters from the data itself, without any prior assumption. The...

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Bibliographic Details
Published in2013 International Workshop on Pattern Recognition in Neuroimaging pp. 216 - 219
Main Authors Dodero, L., Vascon, S., Giancardo, L., Gozzi, A., Sona, D., Murino, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Clustering
Clustering algorithms
Diffusion Magnetic Resonance Imaging
Diffusion tensor imaging
Dominant Sets
DTI
Fibers
Image segmentation
Indexes
Mice
Segmentation
Tractography
White Matter
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Summary:We present an unsupervised approach based on the Dominant Sets framework to automatically segment the white matter fibers into bundles. This framework, rooted in the Game Theory, allows for the automatic determination of the number of clusters from the data itself, without any prior assumption. The clustered bundles are a key information for the generation of unbiased structural connectivity atlases. We have thoroughly validated our algorithm both quantitatively and qualitatively. Indeed, we used biologically plausible synthetic datasets to numerically validate the performance in terms of Precision, Recall and other measures employed in the literature. We also evaluated the algorithm on a real Diffusion Tensor Imaging tractography of a whole mouse brain obtaining promising results. In fact, some of the most prominent brain structures determined by the algorithm correspond to white matter expected anatomy.
DOI:10.1109/PRNI.2013.62