Supervised Segmentation of Fiber Tracts

  • Emanuele Olivetti
  • Paolo Avesani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7005)

Abstract

In this work we study the problem of supervised tract segmentation from tractography data, a vectorial representation of the brain connectivity extracted from diffusion magnetic resonance images. We report a case study based on a dataset where for each tractography of three subjects the segmentation of eight major anatomical tracts was manually operated by expert neuroanatomists. Domain specific distances that encodes the dissimilarity of tracts do not allow to define a positive semi-definite kernel function. We show that a dissimilarity representation based on such distances enables the successful design of a classifier. This approach provides a robust encoding which proves to be effective using a linear classifier. Our empirical analysis shows that we obtain better tract segmentation than previously proposed methods.

Keywords

Uncinate Fasciculus Sequential Minimal Optimization Segmentation Task Arcuate Fasciculus Dissimilarity Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Emanuele Olivetti
    • 1
    • 2
  • Paolo Avesani
    • 1
    • 2
  1. 1.NeuroInformatics Laboratory (NILab)Fondazione Bruno KesslerTrentoItaly
  2. 2.Centro Interdipartimentale Mente e Cervello (CIMeC)Università degli Studi di TrentoItaly

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