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.
|Data di pubblicazione:||2013|
|Titolo:||Automatic White Matter Fiber Clustering Using Dominant Sets|
|Titolo del libro:||2013 International Workshop on Pattern Recognition in Neuroimaging|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/PRNI.2013.62|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|