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Paolo Avesani: Invited Talk at MLINI 2013
Paolo Avesani gave an invited lecture, entitled "Dissimilarity Representation for Unsupervised and Supervised Tract Segmentation", at the Machine Learning and Interpretation in Neuroimaging 2013 conference.
Diffusion magnetic resonance imaging (dMRI) data allow to reconstruct the 3D pathways of axons within the white matter of the brain as a tractography. The analysis of tractographies has drawn attention from the machine learning and pattern recognition communities providing novel challenges such as finding an appropriate representation space for the data. Many of the current learning algorithms require the input to be from a vectorial space. This requirement contrasts with the intrinsic nature of the tractography because its basic elements, called streamlines or tracks, have different lengths and different number of points and for this reason they cannot be directly represented in a common vectorial space.
Dissimilarity representation is an Euclidean embedding technique defined by selecting a set of streamlines called prototypes and then mapping any new streamline to the vector of distances from prototypes. This vectorial encoding becomes the new reference representation space for unsupervised and supervised learning methods to support the segmentation of tractography.